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c1d070d0f0
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/73274 As noticed in https://discuss.pytorch.org/t/calibration-of-model-in-post-training-static-quantization-using-fx-api/143661/6 and related to https://github.com/pytorch/pytorch/issues/72698 when using fx quantizaiton, if an op like view was used in a model and the index parameters were passed in to the ops with a variable rather than hard coded, fx would mistakenly insert observers for them, leading to an error when the observer tried to do tensor only operations on a non-tensor. To fix this, an API was added to specify non tensor arguments for various ops to enable better dtype propagation. NON_TENSOR_ARG_DICT is a nested dict whose first key is a named tuple which contains matching parameters for ops with nontensor args, the inner dict's keys are dtypes and the values are a list of those arg indices that take use such dtypes. Alternatively, instead of a list, the inner dict value can also be a function that takes the node as an argument and returns the list of arg indices. Theoretically this api can support arbitrary functions but the current implmentation is limited to simpler functions given the particular issue this fixes seems to be rare. Note: although torch.unsqueeze and torch.transpose are listed in quantization_patterns.py, those ops appear to be untraceable by fx. I've included tests for their cases but fixing this issue is beyond the scope of this PR Test Plan: python test/test_quantization.py test_non_reference_size ... python test/test_quantization.py test_non_reference_<op> Imported from OSS Reviewed By: jerryzh168 Differential Revision: D34410122 fbshipit-source-id: fc09949ca8a2d6473876a4b6c214eb91e9a9dae2 (cherry picked from commit 3a1375d677b7c98d62b1f5c839645698c39b32b9)
596 lines
24 KiB
Python
596 lines
24 KiB
Python
import re
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import torch
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import torch.nn as nn
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from torch.ao.quantization.utils import is_per_tensor, is_per_channel
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from torch.ao.quantization.quantize import is_activation_post_process
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from torch.fx import GraphModule, map_arg
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from torch.fx.graph import (
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Graph,
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Node,
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)
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from typing import Callable, Optional, List, Dict, Any, Set, Tuple, Union, Type
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from collections import namedtuple
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import operator
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import warnings
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# A dictionary for querying the weight index for a given op
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WEIGHT_INDEX_DICT = {
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torch.nn.functional.conv1d : [1],
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torch.nn.functional.conv2d : [1],
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torch.nn.functional.conv3d : [1],
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torch.nn.functional.linear : [1],
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torch.nn.functional.layer_norm : [2],
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torch.nn.functional.group_norm : [2],
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torch.nn.functional.instance_norm : [3],
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}
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NON_QUANTIZABLE_WEIGHT_OPS = {torch.nn.functional.layer_norm, torch.nn.functional.group_norm, torch.nn.functional.instance_norm}
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BIAS_INDEX_DICT = {
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torch.nn.functional.conv1d : [2],
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torch.nn.functional.conv2d : [2],
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torch.nn.functional.conv3d : [2],
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torch.nn.functional.linear : [2],
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torch.nn.functional.layer_norm : [3],
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torch.nn.functional.group_norm : [3],
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torch.nn.functional.instance_norm : [4],
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}
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def graph_pretty_str(g, shorten=True) -> str:
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"""Returns a printable representation of the ops in the graph of g.
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If shorten is True, tries to abbreviate fields.
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"""
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built_in_func_re = re.compile('<built-in function (.*)>')
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built_in_meth_re = re.compile('<built-in method (.*) of type.*>')
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op_dict = {
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'placeholder': 'plchdr',
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'get_attr': 'gt_prm',
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'call_function': 'cl_fun',
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'call_module': 'cl_mod',
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'call_method': 'cl_meth',
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}
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max_lens = {}
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col_names = ("name", "op", "target", "args", "kwargs")
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for s in col_names:
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max_lens[s] = len(s)
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results = []
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for n in g.nodes:
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# activation_post_process_0 -> obs_0
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name = str(n.name)
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if shorten:
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name = name.replace("activation_post_process", "obs")
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op = str(n.op)
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# placeholder -> plchdr, and so on
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if shorten and op in op_dict:
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op = op_dict[op]
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target = str(n.target)
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# <built-in function foo> -> <bi_fun foo>, and so on
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if shorten:
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built_in_func = built_in_func_re.search(target)
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if built_in_func:
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target = f"<bi_fun {built_in_func.group(1)}>"
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built_in_meth = built_in_meth_re.search(target)
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if built_in_meth:
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target = f"<bi_meth {built_in_meth.group(1)}>"
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target = target.replace("activation_post_process", "obs")
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args = str(n.args)
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if shorten:
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args = args.replace("activation_post_process", "obs")
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kwargs = str(n.kwargs)
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# calculate maximum length of each column, so we can tabulate properly
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for k, v in zip(col_names, (name, op, target, args, kwargs)):
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max_lens[k] = max(max_lens[k], len(v))
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results.append([name, op, target, args, kwargs])
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res_str = ""
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format_str = "{:<{name}} {:<{op}} {:<{target}} {:<{args}} {:<{kwargs}}\n"
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res_str += format_str.format(*col_names, **max_lens)
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for result in results:
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res_str += format_str.format(*result, **max_lens)
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# print an exra note on abbreviations which change attribute names,
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# since users will have to un-abbreviate for further debugging
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if shorten:
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res_str += "*obs_{n} = activation_post_process_{n}\n"
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return res_str
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def get_per_tensor_qparams(activation_post_process):
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assert is_per_tensor(activation_post_process.qscheme), 'Only per tensor quantization is supported'
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scale, zero_point = activation_post_process.calculate_qparams()
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scale = float(scale)
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zero_point = int(zero_point)
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dtype = activation_post_process.dtype
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return scale, zero_point, dtype
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def get_quantize_node_info(activation_post_process: Callable) -> Optional[Tuple[str, Union[Callable, str], Dict[str, Any]]]:
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''' Given an activation_post_process module,
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return node_type(e.g. call_function), quantize op(e.g. quantize_per_tensor) and a dictionary
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of extracted qparams from the module
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'''
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dtype = activation_post_process.dtype # type: ignore[attr-defined]
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compute_dtype = None
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if hasattr(activation_post_process, "compute_dtype"):
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compute_dtype = activation_post_process.compute_dtype # type: ignore[attr-defined]
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quantize_op : Optional[Union[Callable, str]] = None
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if dtype in [torch.quint8, torch.qint8]:
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node_type = "call_function"
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scale, zero_point = activation_post_process.calculate_qparams() # type: ignore[attr-defined]
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if is_per_channel(activation_post_process.qscheme): # type: ignore[attr-defined]
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ch_axis = int(activation_post_process.ch_axis) # type: ignore[attr-defined]
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qparams = {"_scale_": scale, "_zero_point_": zero_point, "_axis_": ch_axis, "_dtype_": dtype}
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quantize_op = torch.quantize_per_channel
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else:
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scale = float(scale)
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zero_point = int(zero_point)
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qparams = {"_scale_": scale, "_zero_point_": zero_point, "_dtype_": dtype}
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quantize_op = torch.quantize_per_tensor
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elif dtype == torch.float16:
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node_type = "call_method"
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quantize_op = "to"
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qparams = {"_dtype_": dtype}
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elif dtype == torch.float32 and compute_dtype in [torch.quint8, torch.qint8, torch.float16]:
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# dynamic quantization
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node_type = "call_function"
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quantize_op = torch.quantize_per_tensor_dynamic
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# TODO: get reduce range from observer
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# reduce_range = activation_post_process.reduce_range
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reduce_range = torch.backends.quantized.engine == "fbgemm"
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qparams = {"_dtype_": compute_dtype, "_reduce_range_": reduce_range}
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else:
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warnings.warn(f"Unsupported activation_post_process in get_quantize_node_info: {activation_post_process}")
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return None
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return node_type, quantize_op, qparams
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def quantize_node(
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in_node: Node,
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obs_module: torch.nn.Module,
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obs_node: Node,
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modules: Dict[str, torch.nn.Module],
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quantized_graph: Graph,
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node_name_to_scope: Dict[str, Tuple[str, type]],
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is_input: bool,
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output_prefix: str = "_output") -> Node:
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''' Add quantization nodes (eg. quantize_per_tensor/per_channel) for given node to graph
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with the qparams calculated from activation_post_process (obs_module).
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The observer node (obs_node) is used to find the FQN of the user of act_post_process.
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e.g. Given input `node` in `node = self.conv(x)`, insert node:
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`quantized_node = torch.quantize_per_tensor(x, self._scale_0, self._zer_point_0, self._dtype_0)`
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where self._scale_0, self._zero_point_0 and self._dtype_0 are
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calculated from `obs_module`
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'''
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# Find the first use of the observer node, we use this to get the scope of the module.
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if is_input:
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# if the quantize function is at the input of op, then we find the first user of the observer_node
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# to get the path. If a linear call_function is in the user list, we return the first instance
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# of linear node to get the FQN.
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users = list(obs_node.users)
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first_linear_use_or_first_use = users[0] if users else None
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linear_node = None
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for n in users:
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if n.op == "call_function" and n.target == torch.nn.functional.linear:
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linear_node = n
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break
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if linear_node:
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first_linear_use_or_first_use = linear_node
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prefix = "_input"
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else:
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# if the quantize function is at the output of the op, we use the observer input node to get the path
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first_linear_use_or_first_use = in_node
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prefix = output_prefix
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if first_linear_use_or_first_use and first_linear_use_or_first_use.name in node_name_to_scope:
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module_path, _ = node_name_to_scope[first_linear_use_or_first_use.name]
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else:
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# TODO: it's not used, so actually we can skip quantization
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# but this requires changing return type of quantize_node
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# we can fix it later if needed
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module_path = ""
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root_module = modules['']
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graph = quantized_graph
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maybe_quantize_node_info = get_quantize_node_info(obs_module)
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assert maybe_quantize_node_info is not None, \
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f"Expecting quantize node info not to be None, observer: {obs_module}"
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node_type, quantize_op, qparams = maybe_quantize_node_info
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inputs = [in_node]
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for key, value in qparams.items():
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if key in ['_scale_', '_zero_point_']:
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# For scale and zero_point values we register them as buffers in the root module.
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qparam_node = create_getattr_from_value(root_module, graph, module_path + prefix + key, value)
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inputs.append(qparam_node)
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else:
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# for qparams that are not scale/zero_point (like axis, dtype) we store them as literals in the graph.
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inputs.append(value)
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return graph.create_node(node_type, quantize_op, tuple(inputs), {})
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def get_custom_module_class_keys(custom_config_dict, custom_config_dict_key) -> List[Any]:
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r""" Get all the unique custom module keys in the custom config dict
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e.g.
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Input:
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custom_config_dict = {
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"float_to_observed_custom_module_class": {
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"static": {
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CustomModule1: ObservedCustomModule
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},
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"dynamic": {
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CustomModule2: DynamicObservedCustomModule
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},
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"weight_only": {
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CustomModule3: WeightOnlyObservedCustomModule
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},
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},
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}
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Output:
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# extract all the keys in "static", "dynamic" and "weight_only" dict
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[CustomModule1, CustomModule2, CustomModule3]
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"""
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# using set to dedup
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float_custom_module_classes : Set[Any] = set()
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custom_module_mapping = custom_config_dict.get(custom_config_dict_key, {})
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for quant_mode in ["static", "dynamic", "weight_only"]:
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quant_mode_custom_module_config = custom_module_mapping.get(quant_mode, {})
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quant_mode_custom_module_classes = set(quant_mode_custom_module_config.keys())
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float_custom_module_classes |= quant_mode_custom_module_classes
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return list(float_custom_module_classes)
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def get_linear_prepack_op_for_dtype(dtype):
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if dtype == torch.float16:
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return torch.ops.quantized.linear_prepack_fp16
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elif dtype == torch.qint8:
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return torch.ops.quantized.linear_prepack
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else:
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raise Exception("can't get linear prepack op for dtype:", dtype)
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def get_qconv_prepack_op(conv_op: Callable) -> Callable:
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prepack_ops = {
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torch.nn.functional.conv1d: torch.ops.quantized.conv1d_prepack,
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torch.nn.functional.conv2d: torch.ops.quantized.conv2d_prepack,
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torch.nn.functional.conv3d: torch.ops.quantized.conv3d_prepack
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}
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prepack_op = prepack_ops.get(conv_op, None)
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assert prepack_op, "Didn't find prepack op for {}".format(conv_op)
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return prepack_op
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def get_qconv_op(conv_op: Callable, has_relu: bool) -> Callable:
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qconv_op = {
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# has relu
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True: {
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torch.nn.functional.conv1d: torch.ops.quantized.conv1d_relu,
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torch.nn.functional.conv2d: torch.ops.quantized.conv2d_relu,
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torch.nn.functional.conv3d: torch.ops.quantized.conv3d_relu
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},
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False: {
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torch.nn.functional.conv1d: torch.ops.quantized.conv1d,
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torch.nn.functional.conv2d: torch.ops.quantized.conv2d,
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torch.nn.functional.conv3d: torch.ops.quantized.conv3d
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}
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}
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qconv = qconv_op[has_relu].get(conv_op)
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assert qconv, "Can't find corresponding quantized conv op for {} {}".format(conv_op, has_relu)
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return qconv
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# Returns a function that can get a new attribute name for module with given
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# prefix, for example,
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# >> get_new_observer_name = get_new_attr_name_with_prefix('_observer')
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# >> new_name = get_new_observer_name(module)
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# new_name will be an unused attribute name on module, e.g. `_observer_1`
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def get_new_attr_name_with_prefix(prefix: str) -> Callable:
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prefix = prefix.replace(".", "_")
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def get_new_attr_name(module: torch.nn.Module):
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def get_attr_name(i: int):
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return prefix + str(i)
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i = 0
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attr_name = get_attr_name(i)
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while hasattr(module, attr_name):
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i += 1
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attr_name = get_attr_name(i)
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return attr_name
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return get_new_attr_name
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def collect_producer_nodes(node: Node) -> Optional[List[Node]]:
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r''' Starting from a target node, trace back until we hit inpu or
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getattr node. This is used to extract the chain of operators
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starting from getattr to the target node, for example
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def forward(self, x):
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observed = self.observer(self.weight)
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return F.linear(x, observed)
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collect_producer_nodes(observed) will either return a list of nodes that
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produces the observed node or None if we can't extract a self contained
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graph without free variables(inputs of the forward function).
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'''
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nodes = [node]
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frontier = [node]
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while frontier:
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node = frontier.pop()
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all_args = list(node.args) + list(node.kwargs.values())
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for arg in all_args:
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if not isinstance(arg, Node):
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continue
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if arg.op == 'placeholder':
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# hit input, can't fold in this case
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return None
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nodes.append(arg)
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if not (arg.op == 'call_function' and arg.target == getattr):
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frontier.append(arg)
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return nodes
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def graph_module_from_producer_nodes(
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root: GraphModule, producer_nodes: List[Node]) -> GraphModule:
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r''' Construct a graph module from extracted producer nodes
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from `collect_producer_nodes` function
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Args:
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root: the root module for the original graph
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producer_nodes: a list of nodes we use to construct the graph
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Return:
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A graph module constructed from the producer nodes
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'''
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assert len(producer_nodes) > 0, 'list of producer nodes can not be empty'
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# since we traced back from node to getattrr
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producer_nodes.reverse()
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graph = Graph()
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env: Dict[Any, Any] = {}
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def load_arg(a):
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return map_arg(a, lambda node: env[node])
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for producer_node in producer_nodes:
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env[producer_node] = graph.node_copy(producer_node, load_arg)
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graph.output(load_arg(producer_nodes[-1]))
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graph_module = GraphModule(root, graph)
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return graph_module
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def assert_and_get_unique_device(module: torch.nn.Module) -> Any:
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"""
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Returns the unique device for a module, or None if no device is found.
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Throws an error if multiple devices are detected.
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"""
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devices = {p.device for p in module.parameters()} | \
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{p.device for p in module.buffers()}
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assert len(devices) <= 1, (
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"prepare only works with cpu or single-device CUDA modules, "
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"but got devices {}".format(devices)
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)
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device = next(iter(devices)) if len(devices) > 0 else None
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return device
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def create_getattr_from_value(module: torch.nn.Module, graph: Graph, prefix: str, value: Any) -> Node:
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"""
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Given a value of any type, creates a getattr node corresponding to the value and
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registers the value as a buffer to the module.
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"""
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get_new_attr_name = get_new_attr_name_with_prefix(prefix)
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attr_name = get_new_attr_name(module)
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device = assert_and_get_unique_device(module)
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module.register_buffer(attr_name, torch.tensor(value, device=device))
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# Create get_attr with value
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attr_node = graph.create_node("get_attr", attr_name)
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return attr_node
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def create_qparam_nodes(
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node_name: str,
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scale: Any,
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zero_point: Any,
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modules: Dict[str, torch.nn.Module],
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quantized_graph: Graph,
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node_name_to_scope: Dict[str, Tuple[str, type]]
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) -> Tuple[Node, Node]:
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"""
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Create getattr nodes in the quantized graph for scale and zero point values.
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The nodes are registered with the root_module of the model.
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"""
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root_module = modules['']
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module_path, _ = node_name_to_scope[node_name]
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scale_node = create_getattr_from_value(root_module, quantized_graph, (module_path + "_scale_"), scale)
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zero_point_node = create_getattr_from_value(root_module, quantized_graph, (module_path + "_zero_point_"), zero_point)
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return (scale_node, zero_point_node)
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def all_node_args_have_no_tensors(node: Node, modules: Dict[str, torch.nn.Module], cache: Dict[Node, bool]) -> bool:
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"""
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If we know for sure that all of this node's args have no
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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
|