pytorch/torch/ao/quantization/fx/utils.py

import copy
import re
import torch
import torch.nn as nn
from torch.ao.quantization import QuantType
from torch.ao.quantization.utils import is_per_tensor, is_per_channel
from torch.ao.quantization.quantize import is_activation_post_process

from torch.fx import GraphModule, map_arg

from torch.fx.graph import (
    Graph,
    Node,
)
from torch.fx.node import Argument
from .custom_config import PrepareCustomConfig

from typing import Callable, Optional, List, Dict, Any, Set, Tuple, Union, Type
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",
    "BIAS_INDEX_DICT",
    "collect_producer_nodes",
    "create_getattr_from_value",
    "create_node_from_old_node_preserve_meta",
    "create_qparam_nodes",
    "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_per_tensor_qparams",
    "get_qconv_op",
    "get_qconv_prepack_op",
    "get_quantize_node_info",
    "graph_module_from_producer_nodes",
    "graph_pretty_str",
    "is_get_tensor_info_node",
    "maybe_get_next_module",
    "NodeInfo",
    "node_return_type_is_int",
    "NON_OBSERVABLE_ARG_DICT",
    "NON_QUANTIZABLE_WEIGHT_OPS",
    "quantize_node",
    "return_arg_list",
    "WEIGHT_INDEX_DICT",
    "get_skipped_module_name_and_classes",
    "get_all_args_as_positional_args",
]


# A dictionary for querying the weight index for a given op
WEIGHT_INDEX_DICT = {
    torch.nn.functional.conv1d : [1],
    torch.nn.functional.conv2d : [1],
    torch.nn.functional.conv3d : [1],
    torch.nn.functional.linear : [1],
    torch.nn.functional.layer_norm : [2],
    torch.nn.functional.group_norm : [2],
    torch.nn.functional.instance_norm : [3],
}

NON_QUANTIZABLE_WEIGHT_OPS = {torch.nn.functional.layer_norm, torch.nn.functional.group_norm, torch.nn.functional.instance_norm}

BIAS_INDEX_DICT = {
    torch.nn.functional.conv1d : [2],
    torch.nn.functional.conv2d : [2],
    torch.nn.functional.conv3d : [2],
    torch.nn.functional.linear : [2],
    torch.nn.functional.layer_norm : [3],
    torch.nn.functional.group_norm : [3],
    torch.nn.functional.instance_norm : [4],
}

def graph_pretty_str(g, shorten=True) -> str:
    """Returns a printable representation of the ops in the graph of g.
    If shorten is True, tries to abbreviate fields.
    """
    built_in_func_re = re.compile('<built-in function (.*)>')
    built_in_meth_re = re.compile('<built-in method (.*) of type.*>')
    op_dict = {
        'placeholder': 'plchdr',
        'get_attr': 'gt_prm',
        'call_function': 'cl_fun',
        'call_module': 'cl_mod',
        'call_method': 'cl_meth',
    }

    max_lens = {}
    col_names = ("name", "op", "target", "args", "kwargs")
    for s in col_names:
        max_lens[s] = len(s)

    results = []
    for n in g.nodes:

        # activation_post_process_0 -> obs_0
        name = str(n.name)
        if shorten:
            name = name.replace("activation_post_process", "obs")

        op = str(n.op)
        # placeholder -> plchdr, and so on
        if shorten and op in op_dict:
            op = op_dict[op]

        target = str(n.target)
        # <built-in function foo> -> <bi_fun foo>, and so on
        if shorten:
            built_in_func = built_in_func_re.search(target)
            if built_in_func:
                target = f"<bi_fun {built_in_func.group(1)}>"
            built_in_meth = built_in_meth_re.search(target)
            if built_in_meth:
                target = f"<bi_meth {built_in_meth.group(1)}>"
            target = target.replace("activation_post_process", "obs")

        args = str(n.args)
        if shorten:
            args = args.replace("activation_post_process", "obs")

        kwargs = str(n.kwargs)

        # calculate maximum length of each column, so we can tabulate properly
        for k, v in zip(col_names, (name, op, target, args, kwargs)):
            max_lens[k] = max(max_lens[k], len(v))
        results.append([name, op, target, args, kwargs])

    res_str = ""
    format_str = "{:<{name}} {:<{op}} {:<{target}} {:<{args}} {:<{kwargs}}\n"
    res_str += format_str.format(*col_names, **max_lens)
    for result in results:
        res_str += format_str.format(*result, **max_lens)

    # print an exra note on abbreviations which change attribute names,
    # since users will have to un-abbreviate for further debugging
    if shorten:
        res_str += "*obs_{n} = activation_post_process_{n}\n"
    return res_str

def get_per_tensor_qparams(activation_post_process):
    assert is_per_tensor(activation_post_process.qscheme), 'Only per tensor quantization is supported'
    scale, zero_point = activation_post_process.calculate_qparams()
    scale = float(scale)
    zero_point = int(zero_point)
    dtype = activation_post_process.dtype
    return scale, zero_point, dtype

def get_quantize_node_info(activation_post_process: Callable) -> Optional[Tuple[str, Union[Callable, str], Dict[str, Any]]]:
    ''' Given an activation_post_process module,
    return node_type(e.g. call_function), quantize op(e.g. quantize_per_tensor) and a dictionary
    of extracted qparams from the module
    '''
    dtype = activation_post_process.dtype  # type: ignore[attr-defined]
    compute_dtype = None
    if hasattr(activation_post_process, "compute_dtype"):
        compute_dtype = activation_post_process.compute_dtype  # type: ignore[attr-defined]
    quantize_op : Optional[Union[Callable, str]] = None
    if dtype in [torch.quint8, torch.qint8]:
        node_type = "call_function"
        scale, zero_point = activation_post_process.calculate_qparams()  # type: ignore[attr-defined]
        if is_per_channel(activation_post_process.qscheme):  # type: ignore[attr-defined]
            ch_axis = int(activation_post_process.ch_axis)  # type: ignore[attr-defined]
            qparams = {"_scale_": scale, "_zero_point_": zero_point, "_axis_": ch_axis, "_dtype_": dtype}
            quantize_op = torch.quantize_per_channel
        else:
            scale = float(scale)
            zero_point = int(zero_point)
            qparams = {"_scale_": scale, "_zero_point_": zero_point, "_dtype_": dtype}
            quantize_op = torch.quantize_per_tensor
    elif dtype == torch.float16:
        node_type = "call_method"
        quantize_op = "to"
        qparams = {"_dtype_": dtype}
    elif dtype == torch.float32 and compute_dtype in [torch.quint8, torch.qint8, torch.float16]:
        # dynamic quantization
        node_type = "call_function"
        quantize_op = torch.quantize_per_tensor_dynamic
        # TODO: get reduce range from observer
        # reduce_range = activation_post_process.reduce_range
        reduce_range = torch.backends.quantized.engine == "fbgemm"
        qparams = {"_dtype_": compute_dtype, "_reduce_range_": reduce_range}
    else:
        warnings.warn(f"Unsupported activation_post_process in get_quantize_node_info: {activation_post_process}")
        return None
    return node_type, quantize_op, qparams

def quantize_node(
        in_node: Node,
        obs_module: torch.nn.Module,
        obs_node: Node,
        modules: Dict[str, torch.nn.Module],
        quantized_graph: Graph,
        node_name_to_scope: Dict[str, Tuple[str, type]],
        is_input: bool,
        output_prefix: str = "_output") -> Node:
    ''' Add quantization nodes (eg. quantize_per_tensor/per_channel) for given node to graph
    with the qparams calculated from activation_post_process (obs_module).
    The observer node (obs_node) is used to find the FQN of the user of act_post_process.
    e.g. Given input `node` in `node = self.conv(x)`, insert node:
    `quantized_node = torch.quantize_per_tensor(x, self._scale_0, self._zer_point_0, self._dtype_0)`
    where self._scale_0, self._zero_point_0 and self._dtype_0 are
    calculated from `obs_module`
    '''
    # Find the first use of the observer node, we use this to get the scope of the module.
    if is_input:
        # if the quantize function is at the input of op, then we find the first user of the observer_node
        # to get the path. If a linear call_function is in the user list, we return the first instance
        # of linear node to get the FQN.
        users = list(obs_node.users)
        first_linear_use_or_first_use = users[0] if users else None
        linear_node = None
        for n in users:
            if n.op == "call_function" and n.target == torch.nn.functional.linear:
                linear_node = n
                break
        if linear_node:
            first_linear_use_or_first_use = linear_node
        prefix = "_input"
    else:
        # if the quantize function is at the output of the op, we use the observer input node to get the path
        first_linear_use_or_first_use = in_node
        prefix = output_prefix

    if first_linear_use_or_first_use and first_linear_use_or_first_use.name in node_name_to_scope:
        module_path, _ = node_name_to_scope[first_linear_use_or_first_use.name]
    else:
        # TODO: it's not used, so actually we can skip quantization
        # but this requires changing return type of quantize_node
        # we can fix it later if needed
        module_path = ""
    root_module = modules['']
    graph = quantized_graph
    maybe_quantize_node_info = get_quantize_node_info(obs_module)
    assert maybe_quantize_node_info is not None, \
        f"Expecting quantize node info not to be None, observer: {obs_module}"
    node_type, quantize_op, qparams = maybe_quantize_node_info
    inputs = [in_node]

    for key, value in qparams.items():
        if key in ['_scale_', '_zero_point_']:
            # For scale and zero_point values we register them as buffers in the root module.
            qparam_node = create_getattr_from_value(root_module, graph, module_path + prefix + key, value)
            inputs.append(qparam_node)
        else:
            # for qparams that are not scale/zero_point (like axis, dtype) we store them as literals in the graph.
            inputs.append(value)
    return graph.create_node(node_type, quantize_op, tuple(inputs), {})

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
    }
    prepack_op = prepack_ops.get(conv_op, None)
    assert prepack_op, "Didn't find prepack op for {}".format(conv_op)
    return prepack_op

def get_qconv_op(conv_op: Callable, has_relu: bool) -> Callable:
    qconv_op = {
        # has relu
        True: {
            torch.nn.functional.conv1d: torch.ops.quantized.conv1d_relu,
            torch.nn.functional.conv2d: torch.ops.quantized.conv2d_relu,
            torch.nn.functional.conv3d: torch.ops.quantized.conv3d_relu
        },
        False: {
            torch.nn.functional.conv1d: torch.ops.quantized.conv1d,
            torch.nn.functional.conv2d: torch.ops.quantized.conv2d,
            torch.nn.functional.conv3d: torch.ops.quantized.conv3d
        }
    }
    qconv = qconv_op[has_relu].get(conv_op)
    assert qconv, "Can't find corresponding quantized conv op for {} {}".format(conv_op, has_relu)
    return qconv

# 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).
    '''
    if isinstance(node, Node) and node.op == "placeholder":
        return None
    nodes = [node]
    frontier = [node]
    while frontier:
        node = frontier.pop()
        if node is None:
            return None
        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 getattrr
    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()}
    assert len(devices) <= 1, (
        "prepare only works with cpu or single-device CUDA modules, "
        "but got devices {}".format(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 create_qparam_nodes(
        node_name: str,
        scale: Any,
        zero_point: Any,
        modules: Dict[str, torch.nn.Module],
        quantized_graph: Graph,
        node_name_to_scope: Dict[str, Tuple[str, type]]
) -> Tuple[Node, Node]:
    """
    Create getattr nodes in the quantized graph for scale and zero point values.
    The nodes are registered with the root_module of the model.
    """
    root_module = modules['']
    module_path, _ = node_name_to_scope[node_name]
    scale_node = create_getattr_from_value(root_module, quantized_graph, (module_path + "_scale_"), scale)
    zero_point_node = create_getattr_from_value(root_module, quantized_graph, (module_path + "_zero_point_"), zero_point)
    return (scale_node, zero_point_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 node_return_type_is_int(node: Node) -> bool:
    """
    Returns true if this node results in an integer, even if some of the args
    are Tensors.
    """
    return node.op == 'call_method' and node.target == 'size'


def is_get_tensor_info_node(node: Node) -> bool:
    """ Returns True if this node is a node that takes a Tensor as input and output some
    meta information about the Tensor, e.g. shape, size etc.
    """
    result: bool = \
        node.op == "call_function" and node.target == getattr and node.args[1] == "shape"  # type: ignore[assignment]
    return result

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.items():
        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 get_all_args_as_positional_args(node: Node) -> List[Argument]:
    """ Get a list of args and kwargs as positional args
    """
    all_args = list(node.args)
    all_args.extend(list(node.kwargs.values()))
    return all_args