mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-14 20:57:59 +00:00
6e1fc5cef8
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/56004 added reference pattern support for GELU, softmax and bmm for int dtypes. For GELU and Softmax, this consisted of adding reference patterns to the default node handler for int dtypes. Note GELU and softmax patterns are not registered since they do not have a proper quantized kernel which means they would either add unnecessary dequant and quant ops to the network, or they would simply error. This can be circumvented with custom qconfig usage as in test_gelu_reference bmm was added within binary ops along with some significant changes to how that code is structured. Theoretically the reference pattern used for bmm could be applied to other dtypes. This was not enabled because of issues relating to Line 1323 in quantize.py. In essence, the prepare step does not know whether an op will use a reference pattern or not, so for ops that are supported with one dtype in reference and one dtype normally, this has the potential to cause issues. This is difficult to get aorund with the is_reference flag being available in the prepare step or discussed changes around separating Test Plan: python test/test_quantization.py TestQuantizeFxOps.test_gelu_reference python test/test_quantization.py TestQuantizeFxOps.ttest_gelu_normal python test/test_quantization.py TestQuantizeFxOps.test_softmax_reference python test/test_quantization.py TestQuantizeFxOps.test_softmax_normal python test/test_quantization.py TestQuantizeFxOps.test_silu_reference python test/test_quantization.py TestQuantizeFxOps.test_bmm_int_reference python test/test_quantization.py TestQuantizeFxOps python test/test_quantization.py TestFuseFx python test/test_quantization.py TestQuantizeFx python test/test_quantization.py TestQuantizeFxModels Imported from OSS Reviewed By: raghuramank100 Differential Revision: D27818340 fbshipit-source-id: de65be0797035463cd2d1b0e4677d1a87f69143c
1142 lines
60 KiB
Python
1142 lines
60 KiB
Python
import torch
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from torch.fx.graph import (
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Node,
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)
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import torch.nn.quantized as nnq
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import torch.nn.quantized.dynamic as nnqd
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from torch.quantization import (
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default_affine_fixed_qparams_fake_quant,
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default_symmetric_fixed_qparams_fake_quant,
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)
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from ..quantization_mappings import (
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get_static_quant_module_class,
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get_dynamic_quant_module_class,
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get_quantized_operator,
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)
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from ..utils import (
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get_swapped_custom_module_class,
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activation_is_statically_quantized,
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activation_is_int8_quantized,
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weight_is_statically_quantized,
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get_qconfig_dtypes,
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)
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from .pattern_utils import (
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register_quant_pattern,
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mark_input_output_not_observed,
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)
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from .utils import (
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_parent_name,
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all_node_args_have_no_tensors,
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quantize_node,
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get_per_tensor_qparams,
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get_linear_prepack_op_for_dtype,
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create_qparam_nodes,
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get_qconv_prepack_op,
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get_qconv_op,
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)
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from .quantization_types import QuantizerCls
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from abc import ABC, abstractmethod
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import operator
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import warnings
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from typing import Any, Callable, Dict, Union, Optional, Tuple, List
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# -------------------------
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# Pattern Registrations
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# -------------------------
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# 1. Post Training Static Quantization and Quantization Aware Training Patterns
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# Base Pattern Handler
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class QuantizeHandler(ABC):
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""" Base handler class for the quantizer patterns
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"""
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def __init__(self, quantizer: QuantizerCls, node: Node):
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""" Records pattern information in __init__, which will be used
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in convert
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"""
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# this is an indicator of whether all the inputs are Node or not
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# since some op might be quantized differently depending on whether
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# all inputs are tensors or not, e.g. add/mul
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self.num_tensor_args = len(node.args)
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self.all_node_args_are_tensors = True
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@abstractmethod
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def convert(self, quantizer: QuantizerCls, node: Node, load_arg: Callable,
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is_reference: bool = False,
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convert_custom_config_dict: Dict[str, Any] = None) -> Node:
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""" Convert the given node to a quantized node and insert
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it to the quantized graph
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"""
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return NotImplemented
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# Binary op configs
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# Supported combinations are:
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# quant_type | activation (compute_type) | weight
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# static quint8 qint8
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# tuple (activation_dtype, weight_dtype, compute_dtype)
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# these are supported types for common binary ops like add/mul etc.
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binary_op_all_dtypes = [
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(torch.quint8, torch.qint8, None),
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(torch.float16, torch.float16, None),
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]
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binary_op_float16_dtypes = [
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(torch.float16, torch.float16, None)
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]
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binary_op_int8_dtypes = [
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(torch.quint8, torch.qint8, None),
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]
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binary_op_supported_dtypes : Dict[Union[Callable, str], List[Tuple[torch.dtype, torch.dtype, None]]] = {
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operator.add: binary_op_all_dtypes,
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torch.add: binary_op_all_dtypes,
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operator.mul: binary_op_all_dtypes,
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torch.mul: binary_op_all_dtypes,
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torch.bmm: binary_op_float16_dtypes,
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torch.sub: binary_op_float16_dtypes,
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operator.sub: binary_op_float16_dtypes,
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torch.div: binary_op_float16_dtypes,
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operator.truediv: binary_op_float16_dtypes,
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torch.sum: binary_op_float16_dtypes
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}
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binary_reference_op_supported_dtypes : Dict[Union[Callable, str], List[Tuple[torch.dtype, torch.dtype, None]]] = {
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torch.bmm: binary_op_int8_dtypes,
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}
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@register_quant_pattern(operator.add)
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@register_quant_pattern(operator.sub)
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@register_quant_pattern(operator.mul)
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@register_quant_pattern(operator.truediv)
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@register_quant_pattern(torch.add)
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@register_quant_pattern(torch.sub)
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@register_quant_pattern(torch.mul)
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@register_quant_pattern(torch.div)
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@register_quant_pattern(torch.sum)
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@register_quant_pattern(torch.bmm)
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@register_quant_pattern((torch.nn.ReLU, operator.add))
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@register_quant_pattern((torch.nn.ReLU, operator.mul))
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@register_quant_pattern((torch.nn.ReLU, torch.add))
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@register_quant_pattern((torch.nn.ReLU, torch.mul))
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@register_quant_pattern((torch.nn.functional.relu, operator.add))
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@register_quant_pattern((torch.nn.functional.relu, operator.mul))
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@register_quant_pattern((torch.nn.functional.relu, torch.add))
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@register_quant_pattern((torch.nn.functional.relu, torch.mul))
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class BinaryOpQuantizeHandler(QuantizeHandler):
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def __init__(self, quantizer: QuantizerCls, node: Node):
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super().__init__(quantizer, node)
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self.relu_node = None
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if (node.op == 'call_function' and node.target is torch.nn.functional.relu) or \
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(node.op == 'call_module' and isinstance(quantizer.modules[node.target], torch.nn.ReLU)):
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self.relu_node = node
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node = node.args[0] # type: ignore
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self.binary_op_node = node
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self.binary_op = node.target
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# determine how many of the first two args are Tensors (versus scalars)
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# this distinguishes things like "x + y" from "x + 2" or "2 + x"
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self.num_tensor_args = 0
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cache_for_no_tensor_check: Dict[Node, bool] = dict()
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for arg_idx in range(len(self.binary_op_node.args)):
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arg = self.binary_op_node.args[arg_idx]
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if isinstance(arg, Node) and (not all_node_args_have_no_tensors(arg, quantizer.modules, cache_for_no_tensor_check)):
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self.num_tensor_args += 1
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self.all_node_args_are_tensors = \
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(self.num_tensor_args == len(self.binary_op_node.args))
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qbin_op_mapping: Dict[Union[Callable, str], Callable] = {
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operator.add: torch.ops.quantized.add,
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torch.add: torch.ops.quantized.add,
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operator.mul: torch.ops.quantized.mul,
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torch.mul: torch.ops.quantized.mul,
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}
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qbin_relu_op_mapping: Dict[Union[Callable, str], Callable] = {
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operator.add: torch.ops.quantized.add_relu,
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torch.add: torch.ops.quantized.add_relu,
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operator.mul: torch.ops.quantized.mul_relu,
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torch.mul: torch.ops.quantized.mul_relu,
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}
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# corresponding quantized op
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self.quantized_binary_op: Optional[Callable] = None
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if self.binary_op in qbin_op_mapping:
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self.quantized_binary_op = qbin_relu_op_mapping[self.binary_op] \
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if self.relu_node is not None \
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else qbin_op_mapping[self.binary_op] # type: ignore
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def convert(self, quantizer: QuantizerCls, node: Node, load_arg: Callable,
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is_reference: bool = False,
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convert_custom_config_dict: Dict[str, Any] = None) -> Node:
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qconfig = quantizer.qconfig_map[node.name]
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dtypes = get_qconfig_dtypes(qconfig)
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if is_reference and self.binary_op in binary_reference_op_supported_dtypes and \
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dtypes in binary_reference_op_supported_dtypes[self.binary_op]:
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if dtypes in binary_op_int8_dtypes:
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args = load_arg(quantized=[0, 1])(node.args)
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args = load_arg(quantized=False)(node.args)
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kwargs = load_arg(quantized=False)(node.kwargs)
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op_out = quantizer.quantized_graph.node_copy(node, load_arg(quantized=False))
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cur_idx = quantizer.activation_post_process_indexes[node.name]
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activation_post_process = \
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quantizer.modules[quantizer.activation_post_process_map[node.name][cur_idx]]
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quantizer.activation_post_process_indexes[node.name] += 1
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return quantize_node(
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quantizer, op_out, activation_post_process,
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node, is_input=False)
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else:
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warnings.warn(
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"No implementation found for dtype combination: {}"
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"for op {} with is_reference={} despite it being listed as supported"
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"this should not happen".format(dtypes, self.binary_op, is_reference))
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return quantizer.quantized_graph.node_copy(node, load_arg(quantized=False))
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elif not is_reference and self.binary_op in binary_op_supported_dtypes and \
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dtypes in binary_op_supported_dtypes[self.binary_op]:
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if dtypes in [(torch.quint8, torch.qint8, None)]:
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assert self.quantized_binary_op is not None
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if self.num_tensor_args == 1:
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# add/mul scalar
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first_arg = self.binary_op_node.args[0]
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cache_for_no_tensor_check: Dict[Node, bool] = dict()
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if isinstance(first_arg, Node) and (
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not all_node_args_have_no_tensors(
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first_arg, quantizer.modules, cache_for_no_tensor_check)):
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quantized_index = 0
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else:
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quantized_index = 1
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return quantizer.quantized_graph.create_node(
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'call_function', self.quantized_binary_op,
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load_arg(quantized=[quantized_index])(self.binary_op_node.args), self.binary_op_node.kwargs)
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else:
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cur_idx = quantizer.activation_post_process_indexes[node.name]
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activation_post_process = \
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quantizer.modules[quantizer.activation_post_process_map[node.name][cur_idx]]
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quantizer.activation_post_process_indexes[node.name] += 1
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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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scale_arg, zero_point_arg = create_qparam_nodes(quantizer, node.name, scale, zero_point)
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if self.relu_node is not None:
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op = torch.ops.quantized.add_relu
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else:
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op = torch.ops.quantized.add
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kwargs = {**self.binary_op_node.kwargs}
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add_args = (*load_arg(quantized=True)(self.binary_op_node.args), scale_arg, zero_point_arg)
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op = quantizer.quantized_graph.create_node(
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'call_function', self.quantized_binary_op, add_args, kwargs)
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return op
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else:
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assert dtypes == (torch.float16, torch.float16, None)
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# TODO (refactor) this is duplicated, maybe have a helper function
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if self.relu_node:
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op_out = quantizer.quantized_graph.node_copy(self.binary_op_node, load_arg(quantized=False))
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relu_args = [op_out]
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relu_args.extend(load_arg(quantized=False)(self.relu_node.args[1:]))
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relu_kwargs = load_arg(quantized=False)(self.relu_node.kwargs)
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return quantizer.quantized_graph.create_node(
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"call_function", torch.nn.functional.relu, tuple(relu_args), relu_kwargs)
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else:
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return quantizer.quantized_graph.node_copy(node, load_arg(quantized=False))
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else:
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# leave the op unquantized if the dtype,reference combination is not supported
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warnings.warn(
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"dtype combination: {} is not "
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"supported by {} for is_reference={}. "
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"Supported non-reference dtype combinations are: {} "
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"Supported reference dtype combinations are: {}"
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"".format(dtypes,
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self.binary_op,
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is_reference,
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binary_op_supported_dtypes[self.binary_op],
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(
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[] if self.binary_op not in binary_reference_op_supported_dtypes.keys()
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else binary_reference_op_supported_dtypes[self.binary_op]
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)
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)
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)
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if self.relu_node:
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op_out = quantizer.quantized_graph.node_copy(self.binary_op_node, load_arg(quantized=False))
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relu_args = [op_out]
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relu_args.extend(load_arg(quantized=False)(self.relu_node.args[1:]))
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relu_kwargs = load_arg(quantized=False)(self.relu_node.kwargs)
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return quantizer.quantized_graph.create_node(
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"call_function", torch.nn.functional.relu, tuple(relu_args), relu_kwargs)
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else:
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return quantizer.quantized_graph.node_copy(node, load_arg(quantized=False))
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@register_quant_pattern(torch.cat)
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class CatQuantizeHandler(QuantizeHandler):
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def convert(self, quantizer: QuantizerCls, node: Node, load_arg: Callable,
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is_reference: bool = False,
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convert_custom_config_dict: Dict[str, Any] = None) -> Node:
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if not self.all_node_args_are_tensors:
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return NotImplemented
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cur_idx = quantizer.activation_post_process_indexes[node.name]
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activation_post_process = \
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quantizer.modules[quantizer.activation_post_process_map[node.name][cur_idx]]
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quantizer.activation_post_process_indexes[node.name] += 1
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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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scale_arg, zero_point_arg = create_qparam_nodes(quantizer, node.name, scale, zero_point)
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kwargs = {**load_arg(quantized=False)(node.kwargs), 'scale': scale_arg, 'zero_point': zero_point_arg}
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return quantizer.quantized_graph.create_node(
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'call_function', torch.ops.quantized.cat, load_arg(quantized=[0])(node.args), kwargs)
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# handle conv, maybe followed by relu
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# NB: matching order is reversed, that is we match from the bottom of this list to the beginning
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@register_quant_pattern(torch.nn.Conv1d)
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@register_quant_pattern(torch.nn.Conv2d)
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@register_quant_pattern(torch.nn.Conv3d)
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@register_quant_pattern(torch.nn.functional.conv1d)
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@register_quant_pattern(torch.nn.functional.conv2d)
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@register_quant_pattern(torch.nn.functional.conv3d)
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# TODO: add qat.Conv1d
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@register_quant_pattern(torch.nn.qat.Conv2d)
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@register_quant_pattern(torch.nn.qat.Conv3d)
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@register_quant_pattern(torch.nn.intrinsic.ConvReLU1d)
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@register_quant_pattern(torch.nn.intrinsic.ConvReLU2d)
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@register_quant_pattern(torch.nn.intrinsic.ConvReLU3d)
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@register_quant_pattern(torch.nn.intrinsic.qat.ConvBn1d)
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@register_quant_pattern(torch.nn.intrinsic.qat.ConvBn2d)
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@register_quant_pattern(torch.nn.intrinsic.qat.ConvBn3d)
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@register_quant_pattern(torch.nn.intrinsic.qat.ConvBnReLU1d)
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@register_quant_pattern(torch.nn.intrinsic.qat.ConvBnReLU2d)
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@register_quant_pattern(torch.nn.intrinsic.qat.ConvBnReLU3d)
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@register_quant_pattern(torch.nn.intrinsic.qat.ConvReLU2d)
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@register_quant_pattern(torch.nn.intrinsic.qat.ConvReLU3d)
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@register_quant_pattern((torch.nn.functional.relu, torch.nn.functional.conv1d))
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@register_quant_pattern((torch.nn.functional.relu, torch.nn.functional.conv2d))
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@register_quant_pattern((torch.nn.functional.relu, torch.nn.functional.conv3d))
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@register_quant_pattern((torch.nn.ReLU, torch.nn.functional.conv1d))
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@register_quant_pattern((torch.nn.ReLU, torch.nn.functional.conv2d))
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@register_quant_pattern((torch.nn.ReLU, torch.nn.functional.conv3d))
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# just for error checks
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@register_quant_pattern((torch.nn.ReLU, torch.nn.Conv2d))
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@register_quant_pattern((torch.nn.ReLU, torch.nn.Conv3d))
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@register_quant_pattern((torch.nn.functional.relu, torch.nn.Conv2d))
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@register_quant_pattern((torch.nn.functional.relu, torch.nn.Conv3d))
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class ConvReluQuantizeHandler(QuantizeHandler):
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def __init__(self, quantizer: QuantizerCls, node: Node):
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super().__init__(quantizer, node)
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self.relu_node = None
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if (node.op == 'call_function' and node.target is torch.nn.functional.relu) or \
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(node.op == 'call_module' and isinstance(quantizer.modules[node.target], torch.nn.ReLU)):
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self.relu_node = node
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node = node.args[0] # type: ignore
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self.conv_node = node
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if node.op == "call_module":
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self.conv = quantizer.modules[self.conv_node.target]
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elif node.op == "call_function":
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self.conv = node.target
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def convert(self, quantizer: QuantizerCls, node: Node, load_arg: Callable,
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is_reference: bool = False,
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convert_custom_config_dict: Dict[str, Any] = None) -> Node:
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# Supported combinations are:
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# quant_type | activation (compute_type) | weight
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# static quint8 qint8
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# tuple (activation_dtype, weight_dtype, compute_dtype)
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supported_dtypes = [
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(torch.quint8, torch.qint8, None),
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]
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# TODO: is_reference option for conv module
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qconfig = quantizer.qconfig_map[node.name]
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dtypes = get_qconfig_dtypes(qconfig)
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# leave the op unquantized if the dtype combination is not supported
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if dtypes not in supported_dtypes:
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warnings.warn(
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"dtype combination: {} is not "
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"supported by Conv "
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"supported dtype combinations are: {}".format(dtypes, supported_dtypes))
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if self.relu_node:
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conv_out = quantizer.quantized_graph.node_copy(self.conv_node, load_arg(quantized=False))
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relu_args = [conv_out]
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relu_args.extend(load_arg(quantized=False)(self.relu_node.args[1:]))
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relu_kwargs = load_arg(quantized=False)(self.relu_node.kwargs)
|
|
return quantizer.quantized_graph.create_node(
|
|
"call_function", torch.nn.functional.relu, tuple(relu_args), relu_kwargs)
|
|
else:
|
|
return quantizer.quantized_graph.node_copy(node, load_arg(quantized=False))
|
|
|
|
activation_int8_quantized = activation_is_int8_quantized(qconfig)
|
|
|
|
if self.conv_node.op == 'call_module':
|
|
# note that relu should already be fused into conv module in the fusion step
|
|
assert self.relu_node is None, 'conv module and relu fusion is not executed, ' \
|
|
'please make sure to run fusion before prepare'
|
|
if convert_custom_config_dict is None:
|
|
convert_custom_config_dict = {}
|
|
additional_static_quant_mapping = convert_custom_config_dict.get("static", {})
|
|
# 1. attach activation post process to module
|
|
cur_idx = quantizer.activation_post_process_indexes[node.name]
|
|
self.conv.activation_post_process = \
|
|
quantizer.modules[quantizer.activation_post_process_map[node.name][cur_idx]]
|
|
quantizer.activation_post_process_indexes[node.name] += 1
|
|
# 2. select quantized class
|
|
qconv_cls = get_static_quant_module_class(
|
|
type(self.conv), additional_static_quant_mapping)
|
|
quantized = qconv_cls.from_float(self.conv)
|
|
parent_name, name = _parent_name(self.conv_node.target)
|
|
setattr(quantizer.modules[parent_name], name, quantized)
|
|
return quantizer.quantized_graph.create_node(
|
|
'call_module',
|
|
self.conv_node.target,
|
|
(load_arg(quantized=True)(self.conv_node.args[0]),),
|
|
{})
|
|
else: # call_function
|
|
assert self.conv_node.op == "call_function"
|
|
if is_reference:
|
|
args = load_arg(quantized=[0, 1])(self.conv_node.args)
|
|
args = load_arg(quantized=False)(self.conv_node.args)
|
|
kwargs = load_arg(quantized=False)(self.conv_node.kwargs)
|
|
op_out = quantizer.quantized_graph.create_node(
|
|
"call_function", self.conv, args, kwargs)
|
|
if self.relu_node:
|
|
relu_args = [op_out]
|
|
relu_args.extend(load_arg(quantized=False)(self.relu_node.args[1:]))
|
|
relu_kwargs = load_arg(quantized=False)(self.relu_node.kwargs)
|
|
op_out = quantizer.quantized_graph.create_node(
|
|
"call_function", torch.nn.functional.relu, tuple(relu_args), relu_kwargs)
|
|
|
|
if activation_int8_quantized:
|
|
root_module = quantizer.modules['']
|
|
act_post_process_name = self.relu_node.name if self.relu_node else self.conv_node.name
|
|
act_post_process_node = self.relu_node if self.relu_node else self.conv_node
|
|
cur_idx = quantizer.activation_post_process_indexes[act_post_process_name]
|
|
activation_post_process = \
|
|
quantizer.modules[quantizer.activation_post_process_map[act_post_process_name][cur_idx]]
|
|
quantizer.activation_post_process_indexes[act_post_process_name] += 1
|
|
return quantize_node(
|
|
quantizer, op_out, activation_post_process,
|
|
act_post_process_node, is_input=False)
|
|
else:
|
|
# output for dynamically quantized conv op is not quantized
|
|
return op_out
|
|
else:
|
|
assert len(self.conv_node.args) >= 7, \
|
|
"only conv2d calls with all arguments specified is supported right now in is_reference=False option"
|
|
args = load_arg(quantized=[0, 1])(self.conv_node.args)
|
|
# pack weight
|
|
weight = load_arg(quantized=True)(self.conv_node.args[1])
|
|
other_args = load_arg(quantized=False)(self.conv_node.args[2:])
|
|
bias, stride, padding, dilation, groups = other_args
|
|
if self.conv == torch.nn.functional.conv1d:
|
|
# F.conv1d can take `int` as well as `list[int]` for stride,
|
|
# padding, dilation, but the prepack op cannot. Convert
|
|
# these to lists if needed.
|
|
stride = [stride] if isinstance(stride, int) else stride
|
|
padding = [padding] if isinstance(padding, int) else padding
|
|
dilation = [dilation] if isinstance(dilation, int) else dilation
|
|
prepack_args = (weight, bias, stride, padding, dilation, groups)
|
|
prepack_op = get_qconv_prepack_op(self.conv)
|
|
packed_weight = quantizer.quantized_graph.create_node(
|
|
"call_function", prepack_op, prepack_args, {})
|
|
assert activation_int8_quantized, \
|
|
"currently only static quantization is supported for conv"
|
|
# construct conv input
|
|
if activation_int8_quantized:
|
|
qconv_op = get_qconv_op(self.conv, self.relu_node is not None)
|
|
conv_input = load_arg(quantized=True)(self.conv_node.args[0])
|
|
act_post_process_name = self.relu_node.name if self.relu_node else self.conv_node.name
|
|
cur_idx = quantizer.activation_post_process_indexes[act_post_process_name]
|
|
activation_post_process = \
|
|
quantizer.modules[quantizer.activation_post_process_map[act_post_process_name][cur_idx]]
|
|
quantizer.activation_post_process_indexes[act_post_process_name] += 1
|
|
scale, zero_point, _ = get_per_tensor_qparams(activation_post_process)
|
|
scale_node, zero_point_node = create_qparam_nodes(quantizer, self.conv_node.name, scale, zero_point)
|
|
qconv_args = (conv_input, packed_weight, scale_node, zero_point_node)
|
|
kwargs = load_arg(quantized=False)(self.conv_node.kwargs)
|
|
op = quantizer.quantized_graph.create_node(
|
|
'call_function', qconv_op, qconv_args, kwargs)
|
|
# Store the name of the fused op to get the path of node after fusion as well.
|
|
# TODO: may need to change the key to Node regenerate the map in each transformation,
|
|
# since we might not be able to rely on the name
|
|
quantizer.node_name_to_scope[op.name] = quantizer.node_name_to_scope[self.conv_node.name]
|
|
return op
|
|
else:
|
|
# conv2d_dyanmic branch
|
|
raise Exception("Only static quant is supported for conv")
|
|
|
|
|
|
# handle linear, maybe followed by relu
|
|
@register_quant_pattern(torch.nn.Linear)
|
|
@register_quant_pattern(torch.nn.functional.linear)
|
|
@register_quant_pattern(torch.nn.qat.Linear)
|
|
@register_quant_pattern(torch.nn.intrinsic.LinearReLU)
|
|
@register_quant_pattern(torch.nn.intrinsic.qat.LinearReLU)
|
|
@register_quant_pattern((torch.nn.functional.relu, torch.nn.functional.linear))
|
|
@register_quant_pattern((torch.nn.ReLU, torch.nn.functional.linear))
|
|
# for error checks
|
|
@register_quant_pattern((torch.nn.ReLU, torch.nn.Linear))
|
|
@register_quant_pattern((torch.nn.functional.relu, torch.nn.Linear))
|
|
class LinearReLUQuantizeHandler(QuantizeHandler):
|
|
def __init__(self, quantizer: QuantizerCls, node: Node):
|
|
super().__init__(quantizer, node)
|
|
self.relu_node = None
|
|
if (node.op == 'call_function' and node.target is torch.nn.functional.relu) or \
|
|
(node.op == 'call_module' and isinstance(quantizer.modules[node.target], torch.nn.ReLU)):
|
|
self.relu_node = node
|
|
node = node.args[0] # type: ignore
|
|
self.linear_node = node
|
|
if node.op == 'call_module':
|
|
self.linear = quantizer.modules[self.linear_node.target]
|
|
|
|
def convert(self, quantizer: QuantizerCls, node: Node, load_arg: Callable,
|
|
is_reference: bool = False,
|
|
convert_custom_config_dict: Dict[str, Any] = None) -> Node:
|
|
# Supported combinations are:
|
|
# quant_type | activation (compute_type) | weight
|
|
# static quint8 qint8
|
|
# dynamic float32 (quint8) qint8
|
|
# weight_only float32 float16
|
|
# tuple (activation_dtype, weight_dtype, compute_dtype)
|
|
supported_dtypes = [
|
|
(torch.quint8, torch.qint8, None),
|
|
(torch.float32, torch.qint8, torch.quint8),
|
|
(torch.float32, torch.float16, None),
|
|
# static float16 quantization
|
|
(torch.float16, torch.float16, None),
|
|
]
|
|
qconfig = quantizer.qconfig_map[node.name]
|
|
dtypes = get_qconfig_dtypes(qconfig)
|
|
# leave the op unquantized if the dtype combination is not supported
|
|
if dtypes not in supported_dtypes:
|
|
warnings.warn(
|
|
"dtype combination: {} is not "
|
|
"supported by Linear "
|
|
"supported dtype combinations are: {}".format(dtypes, supported_dtypes))
|
|
if self.relu_node:
|
|
op_out = quantizer.quantized_graph.node_copy(self.linear_node, load_arg(quantized=False))
|
|
relu_args = [op_out]
|
|
relu_args.extend(load_arg(quantized=False)(self.relu_node.args[1:]))
|
|
relu_kwargs = load_arg(quantized=False)(self.relu_node.kwargs)
|
|
return quantizer.quantized_graph.create_node(
|
|
"call_function", torch.nn.functional.relu, tuple(relu_args), relu_kwargs)
|
|
else:
|
|
return quantizer.quantized_graph.node_copy(node, load_arg(quantized=None))
|
|
|
|
activation_int8_quantized = activation_is_int8_quantized(qconfig)
|
|
weight_dtype = dtypes[1]
|
|
# TODO: reference_model option for linear module
|
|
if self.linear_node.op == 'call_module':
|
|
# note that relu should already be fused into conv module in the fusion step
|
|
assert self.relu_node is None, 'linear module and relu fusion is not executed, ' \
|
|
'please make sure to run fusion before prepare'
|
|
# 1. attach output activation post process to linear module
|
|
if node.name in quantizer.activation_post_process_map:
|
|
# this is the static quantization case
|
|
cur_idx = quantizer.activation_post_process_indexes[node.name]
|
|
output_activation_post_process = \
|
|
quantizer.modules[quantizer.activation_post_process_map[node.name][cur_idx]]
|
|
quantizer.activation_post_process_indexes[node.name] += 1
|
|
else:
|
|
output_activation_post_process = None
|
|
|
|
if output_activation_post_process:
|
|
self.linear.activation_post_process = output_activation_post_process
|
|
|
|
# 2. select corresponding quantized linear class for the float linear class
|
|
if type(self.linear) in [torch.nn.Linear, torch.nn.qat.Linear]:
|
|
qlinear = nnq.Linear if activation_int8_quantized else nnqd.Linear
|
|
elif type(self.linear) in [torch.nn.intrinsic.LinearReLU, torch.nn.intrinsic.qat.LinearReLU]:
|
|
assert activation_int8_quantized, \
|
|
'Only int8 static quantization is supported for LinearReLU'
|
|
qlinear = torch.nn.intrinsic.quantized.LinearReLU
|
|
else:
|
|
raise Exception("unhandled linear type:", type(self.linear))
|
|
quantized = qlinear.from_float(self.linear)
|
|
parent_name, name = _parent_name(self.linear_node.target)
|
|
setattr(quantizer.modules[parent_name], name, quantized)
|
|
# activation needs to be quantized for static quantization
|
|
return quantizer.quantized_graph.create_node(
|
|
'call_module',
|
|
self.linear_node.target,
|
|
(load_arg(quantized=activation_int8_quantized)(self.linear_node.args[0]),), {})
|
|
else: # call_function
|
|
assert self.linear_node.op == 'call_function'
|
|
if is_reference:
|
|
quantized_input_idxs = []
|
|
if activation_int8_quantized:
|
|
quantized_input_idxs.append(0)
|
|
if weight_is_statically_quantized(qconfig):
|
|
quantized_input_idxs.append(1)
|
|
args = load_arg(quantized=quantized_input_idxs)(self.linear_node.args)
|
|
args = load_arg(quantized=False)(self.linear_node.args)
|
|
kwargs = load_arg(quantized=False)(self.linear_node.kwargs)
|
|
op_out = quantizer.quantized_graph.create_node(
|
|
"call_function", torch.nn.functional.linear, args, kwargs)
|
|
if self.relu_node:
|
|
relu_args = [op_out]
|
|
relu_args.extend(load_arg(quantized=False)(self.relu_node.args[1:]))
|
|
relu_kwargs = load_arg(quantized=False)(self.relu_node.kwargs)
|
|
op_out = quantizer.quantized_graph.create_node(
|
|
"call_function", torch.nn.functional.relu, tuple(relu_args), relu_kwargs)
|
|
|
|
if activation_int8_quantized:
|
|
# quantize output for statically quantized linear op
|
|
root_module = quantizer.modules['']
|
|
act_post_process_name = self.relu_node.name if self.relu_node else self.linear_node.name
|
|
act_post_process_node = self.relu_node if self.relu_node else self.linear_node
|
|
cur_idx = quantizer.activation_post_process_indexes[act_post_process_name]
|
|
activation_post_process = \
|
|
quantizer.modules[quantizer.activation_post_process_map[act_post_process_name][cur_idx]]
|
|
quantizer.activation_post_process_indexes[act_post_process_name] += 1
|
|
return quantize_node(
|
|
quantizer,
|
|
op_out,
|
|
activation_post_process,
|
|
act_post_process_node,
|
|
is_input=False)
|
|
else:
|
|
# output for dynamically quantized linear op is not quantized
|
|
return op_out
|
|
else: # non-reference option
|
|
# prepacking weights for static int8 quant and dynamic quant
|
|
if dtypes != (torch.float16, torch.float16, None):
|
|
# linear args
|
|
# (x, weight, bias, ...)
|
|
weight_quantized = weight_is_statically_quantized(qconfig)
|
|
linear_weight = load_arg(quantized=weight_quantized)(self.linear_node.args[1])
|
|
|
|
# get other arguments
|
|
kwargs = {**load_arg(quantized=False)(self.linear_node.kwargs)}
|
|
# pack weight
|
|
bias = None
|
|
# all args after bias, including bias
|
|
other_args = load_arg(quantized=False)(self.linear_node.args[2:])
|
|
if len(self.linear_node.args) > 2:
|
|
bias = load_arg(quantized=False)(self.linear_node.args[2])
|
|
other_args = other_args[1:] # remove the bias argument
|
|
else:
|
|
assert 'bias' in kwargs, \
|
|
'expect bias provided as a keyword argument when it is not a positional argument'
|
|
bias = kwargs['bias']
|
|
kwargs.pop('bias')
|
|
prepack_args = (linear_weight, bias)
|
|
prepack_op = get_linear_prepack_op_for_dtype(weight_dtype)
|
|
packed_weight = quantizer.quantized_graph.create_node(
|
|
'call_function', prepack_op, prepack_args, {})
|
|
# construct linear input
|
|
if activation_int8_quantized:
|
|
qlinear_op = torch.ops.quantized.linear_relu if self.relu_node else torch.ops.quantized.linear
|
|
linear_input = load_arg(quantized=True)(self.linear_node.args[0])
|
|
act_post_process_name = self.relu_node.name if self.relu_node else self.linear_node.name
|
|
cur_idx = quantizer.activation_post_process_indexes[act_post_process_name]
|
|
activation_post_process = \
|
|
quantizer.modules[quantizer.activation_post_process_map[act_post_process_name][cur_idx]]
|
|
quantizer.activation_post_process_indexes[act_post_process_name] += 1
|
|
scale, zero_point, _ = get_per_tensor_qparams(activation_post_process)
|
|
|
|
scale_node, zero_point_node = create_qparam_nodes(quantizer, self.linear_node.name, scale, zero_point)
|
|
|
|
qlinear_args = (linear_input, packed_weight, scale_node, zero_point_node)
|
|
op = quantizer.quantized_graph.create_node(
|
|
"call_function", qlinear_op, qlinear_args, kwargs)
|
|
# Store the name of the fused op to get the path of node after fusion as well.
|
|
# TODO: may need to change the key to Node regenerate the map in each transformation,
|
|
# since we might not be able to rely on the name
|
|
quantizer.node_name_to_scope[op.name] = quantizer.node_name_to_scope[self.linear_node.name]
|
|
return op
|
|
elif dtypes in [(torch.float32, torch.qint8, torch.quint8),
|
|
(torch.float32, torch.float16, None)]:
|
|
# choose linear dynamic or linear dynamic fp16 op based on weight dtype
|
|
qlinear_op = torch.ops.quantized.linear_dynamic \
|
|
if weight_dtype == torch.qint8 \
|
|
else torch.ops.quantized.linear_dynamic_fp16
|
|
linear_input = load_arg(quantized=False)(self.linear_node.args[0])
|
|
qlinear_args = (linear_input, packed_weight) # type: ignore
|
|
op_out = quantizer.quantized_graph.create_node(
|
|
"call_function", qlinear_op, qlinear_args, kwargs)
|
|
# Store the name of the dynamic op to get the path of node after replacement as well.
|
|
# TODO: may need to change the key to Node regenerate the map in each transformation,
|
|
# since we might not be able to rely on the name
|
|
quantizer.node_name_to_scope[op_out.name] = quantizer.node_name_to_scope[self.linear_node.name]
|
|
if self.relu_node:
|
|
op_out = quantizer.quantized_graph.create_node("call_function", torch.nn.functional.relu, (op_out,), {})
|
|
return op_out
|
|
else:
|
|
assert dtypes == (torch.float16, torch.float16, None)
|
|
# TODO (refactor) this is duplicated, maybe have a helper function
|
|
if self.relu_node:
|
|
op_out = quantizer.quantized_graph.node_copy(self.linear_node, load_arg(quantized=False))
|
|
relu_args = [op_out]
|
|
relu_args.extend(load_arg(quantized=False)(self.relu_node.args[1:]))
|
|
relu_kwargs = load_arg(quantized=False)(self.relu_node.kwargs)
|
|
return quantizer.quantized_graph.create_node(
|
|
"call_function", torch.nn.functional.relu, tuple(relu_args), relu_kwargs)
|
|
else:
|
|
return quantizer.quantized_graph.node_copy(node, load_arg(quantized=False))
|
|
|
|
@register_quant_pattern(torch.nn.BatchNorm2d)
|
|
@register_quant_pattern(torch.nn.BatchNorm3d)
|
|
@register_quant_pattern(torch.nn.intrinsic.BNReLU2d)
|
|
@register_quant_pattern(torch.nn.intrinsic.BNReLU3d)
|
|
class BatchNormQuantizeHandler(QuantizeHandler):
|
|
def __init__(self, quantizer: QuantizerCls, node: Node):
|
|
super().__init__(quantizer, node)
|
|
assert node.op == 'call_module'
|
|
self.bn_node = node
|
|
self.bn = quantizer.modules[self.bn_node.target]
|
|
|
|
def convert(self, quantizer: QuantizerCls, node: Node, load_arg: Callable,
|
|
is_reference: bool = False,
|
|
convert_custom_config_dict: Dict[str, Any] = None) -> Node:
|
|
if convert_custom_config_dict is None:
|
|
convert_custom_config_dict = {}
|
|
additional_static_quant_mapping = convert_custom_config_dict.get("static", {})
|
|
# 1. attach activation post process to module
|
|
cur_idx = quantizer.activation_post_process_indexes[node.name]
|
|
self.bn.activation_post_process = \
|
|
quantizer.modules[quantizer.activation_post_process_map[node.name][cur_idx]]
|
|
quantizer.activation_post_process_indexes[node.name] += 1
|
|
qbn_cls = get_static_quant_module_class(type(self.bn), additional_static_quant_mapping)
|
|
quantized = qbn_cls.from_float(self.bn)
|
|
parent_name, name = _parent_name(self.bn_node.target)
|
|
setattr(quantizer.modules[parent_name], name, quantized)
|
|
return quantizer.quantized_graph.create_node(
|
|
'call_module',
|
|
self.bn_node.target,
|
|
load_arg(quantized=[0])(self.bn_node.args),
|
|
load_arg(quantized=False)(self.bn_node.kwargs))
|
|
|
|
@register_quant_pattern(torch.nn.Embedding)
|
|
@register_quant_pattern(torch.nn.EmbeddingBag)
|
|
@mark_input_output_not_observed()
|
|
class EmbeddingQuantizeHandler(QuantizeHandler):
|
|
def __init__(self, quantizer: QuantizerCls, node: Node):
|
|
super().__init__(quantizer, node)
|
|
|
|
def convert(self, quantizer: QuantizerCls, node: Node, load_arg: Callable,
|
|
is_reference: bool = False,
|
|
convert_custom_config_dict: Dict[str, Any] = None) -> Node:
|
|
# Supported combinations are:
|
|
# quant_type | activation | weight | activation_compute_type
|
|
# weight_only | float32 | quint8 | None
|
|
# weight_only | float32 | quint4x2 | None
|
|
# tuple (activation_dtype, weight_dtype, compute_dtype)
|
|
supported_dtypes = [
|
|
(torch.float32, torch.quint8, None),
|
|
(torch.float32, torch.quint4x2, None),
|
|
]
|
|
assert node.op == 'call_module'
|
|
emb_node = node
|
|
qconfig = quantizer.qconfig_map[node.name]
|
|
dtypes = get_qconfig_dtypes(qconfig)
|
|
# leave the op unquantized if the dtype combination is not supported
|
|
if dtypes not in supported_dtypes:
|
|
warnings.warn(
|
|
"dtype combination: {} is not "
|
|
"supported by Embedding/EmbeddingBag, "
|
|
"supported dtype combinations are: {}".format(dtypes, supported_dtypes))
|
|
return quantizer.quantized_graph.node_copy(node, load_arg(quantized=None))
|
|
|
|
emb = quantizer.modules[emb_node.target]
|
|
qemb = get_static_quant_module_class(type(emb))
|
|
quantized = qemb.from_float(emb)
|
|
parent_name, name = _parent_name(emb_node.target)
|
|
setattr(quantizer.modules[parent_name], name, quantized)
|
|
return quantizer.quantized_graph.create_node(
|
|
'call_module',
|
|
emb_node.target,
|
|
load_arg(quantized=False)(emb_node.args),
|
|
load_arg(quantized=False)(emb_node.kwargs))
|
|
|
|
# TODO (maybe): merge with embedding quantize handler
|
|
@register_quant_pattern(torch.nn.GRUCell)
|
|
@register_quant_pattern(torch.nn.LSTMCell)
|
|
@register_quant_pattern(torch.nn.RNNCell)
|
|
@register_quant_pattern(torch.nn.LSTM)
|
|
@mark_input_output_not_observed()
|
|
class RNNDynamicQuantizeHandler(QuantizeHandler):
|
|
def __init__(self, quantizer: QuantizerCls, node: Node):
|
|
super().__init__(quantizer, node)
|
|
|
|
def convert(self, quantizer: QuantizerCls, node: Node, load_arg: Callable,
|
|
is_reference: bool = False,
|
|
convert_custom_config_dict: Dict[str, Any] = None) -> Node:
|
|
# Supported combinations are:
|
|
# quant_type | activation | weight | activation_compute_type
|
|
# dynamic | float32 | qint8 | quint8
|
|
# dynamic | float32 | float16 | None
|
|
# tuple (activation_dtype, weight_dtype, compute_dtype)
|
|
supported_dtypes = [
|
|
(torch.float32, torch.qint8, torch.quint8),
|
|
(torch.float32, torch.float16, None),
|
|
]
|
|
assert node.op == 'call_module'
|
|
qconfig = quantizer.qconfig_map[node.name]
|
|
dtypes = get_qconfig_dtypes(qconfig)
|
|
# leave the op unquantized if the dtype combination is not supported
|
|
if dtypes not in supported_dtypes:
|
|
warnings.warn(
|
|
"dtype combination: {} is not "
|
|
"supported by Embedding/EmbeddingBag, "
|
|
"supported dtype combinations are: {}".format(dtypes, supported_dtypes))
|
|
return quantizer.quantized_graph.node_copy(node, load_arg(quantized=None))
|
|
|
|
module = quantizer.modules[node.target]
|
|
qmodule_cls = get_dynamic_quant_module_class(type(module))
|
|
qmodule = qmodule_cls.from_float(module)
|
|
parent_name, name = _parent_name(node.target)
|
|
setattr(quantizer.modules[parent_name], name, qmodule)
|
|
return quantizer.quantized_graph.create_node(
|
|
'call_module',
|
|
node.target,
|
|
load_arg(quantized=False)(node.args),
|
|
load_arg(quantized=False)(node.kwargs))
|
|
|
|
ARGS_TO_SKIP = {
|
|
torch._ops.ops.quantized.hardswish: ['inplace'],
|
|
torch._ops.ops.quantized.instance_norm:
|
|
['running_mean', 'running_var', 'use_input_stats', 'momentum'],
|
|
}
|
|
@register_quant_pattern(torch.nn.ConvTranspose1d)
|
|
@register_quant_pattern(torch.nn.ConvTranspose2d)
|
|
@register_quant_pattern(torch.nn.ELU)
|
|
@register_quant_pattern(torch.nn.LeakyReLU)
|
|
@register_quant_pattern(torch.nn.Hardswish)
|
|
@register_quant_pattern(torch.nn.InstanceNorm1d)
|
|
@register_quant_pattern(torch.nn.InstanceNorm2d)
|
|
@register_quant_pattern(torch.nn.InstanceNorm3d)
|
|
@register_quant_pattern(torch.nn.LayerNorm)
|
|
@register_quant_pattern(torch.nn.SiLU)
|
|
# we currently only support reference patterns for these ops so they have been removed
|
|
# until they receive a proper fp16 kernel. To use the reference pattern, use a custom qconfig
|
|
# @register_quant_pattern(torch.nn.GELU)
|
|
# @register_quant_pattern(torch.nn.Softmax)
|
|
@register_quant_pattern(torch.nn.functional.hardswish)
|
|
@register_quant_pattern(torch.nn.functional.instance_norm)
|
|
@register_quant_pattern(torch.nn.functional.layer_norm)
|
|
@register_quant_pattern(torch.nn.functional.leaky_relu)
|
|
@register_quant_pattern(torch.nn.functional.silu)
|
|
# we currently only support reference patterns for these ops so they have been removed
|
|
# until they receive a proper fp16 kernel. To use the reference pattern, use a custom qconfig
|
|
# @register_quant_pattern(torch.nn.functional.gelu)
|
|
# @register_quant_pattern(torch.nn.functional.softmax)
|
|
class DefaultNodeQuantizeHandler(QuantizeHandler):
|
|
''' Common quantized op, first input and first output will be quantized
|
|
'''
|
|
|
|
def __init__(self, quantizer: QuantizerCls, node: Node):
|
|
super().__init__(quantizer, node)
|
|
if node.op == "call_function" or node.op == "call_method":
|
|
self.op = node.target
|
|
elif node.op == "call_module":
|
|
self.op = type(quantizer.modules[node.target])
|
|
|
|
def convert(self, quantizer: QuantizerCls, node: Node, load_arg: Callable,
|
|
is_reference: bool = False,
|
|
convert_custom_config_dict: Dict[str, Any] = None) -> Node:
|
|
if not self.all_node_args_are_tensors:
|
|
return NotImplemented
|
|
assert node.op in ['call_module', 'call_function'], 'Only call_module and ' + \
|
|
'call_function are handled in DefaultNode'
|
|
if convert_custom_config_dict is None:
|
|
convert_custom_config_dict = {}
|
|
additional_static_quant_mapping = convert_custom_config_dict.get("static", {})
|
|
|
|
all_dtypes = [
|
|
(torch.quint8, torch.qint8, None),
|
|
(torch.float16, torch.float16, None)
|
|
]
|
|
int8_dtypes = [
|
|
(torch.quint8, torch.qint8, None)
|
|
]
|
|
fp16_dtypes = [
|
|
(torch.float16, torch.float16, None)
|
|
]
|
|
supported_dtypes = {
|
|
torch.nn.ConvTranspose1d: int8_dtypes,
|
|
torch.nn.ConvTranspose2d: int8_dtypes,
|
|
torch.nn.ELU: int8_dtypes,
|
|
torch.nn.LeakyReLU: int8_dtypes,
|
|
torch.nn.Hardswish: int8_dtypes,
|
|
torch.nn.InstanceNorm1d: int8_dtypes,
|
|
torch.nn.InstanceNorm2d: int8_dtypes,
|
|
torch.nn.InstanceNorm3d: int8_dtypes,
|
|
torch.nn.LayerNorm: all_dtypes,
|
|
torch.nn.SiLU: fp16_dtypes,
|
|
torch.nn.GELU: int8_dtypes,
|
|
torch.nn.Softmax: int8_dtypes,
|
|
torch.nn.functional.hardswish: int8_dtypes,
|
|
torch.nn.functional.instance_norm: int8_dtypes,
|
|
torch.nn.functional.layer_norm: all_dtypes,
|
|
torch.nn.functional.leaky_relu: int8_dtypes,
|
|
torch.nn.functional.silu: fp16_dtypes,
|
|
torch.nn.functional.gelu: int8_dtypes,
|
|
torch.nn.functional.softmax: int8_dtypes,
|
|
}
|
|
qconfig = quantizer.qconfig_map[node.name]
|
|
dtypes = get_qconfig_dtypes(qconfig)
|
|
if dtypes not in supported_dtypes[self.op]:
|
|
warnings.warn(
|
|
"dtype combination: {} is not "
|
|
"supported by {} "
|
|
"supported dtype combinations are: {}".format(dtypes, self.op, supported_dtypes[self.op]))
|
|
return quantizer.quantized_graph.node_copy(node, load_arg(quantized=False))
|
|
# TODO: make helper functions for (torch.quint8, torch.qint8, None)
|
|
if not is_reference:
|
|
if dtypes in [(torch.quint8, torch.qint8, None)]:
|
|
cur_idx = quantizer.activation_post_process_indexes[node.name]
|
|
activation_post_process = \
|
|
quantizer.modules[quantizer.activation_post_process_map[node.name][cur_idx]]
|
|
quantizer.activation_post_process_indexes[node.name] += 1
|
|
if node.op == 'call_module':
|
|
module = quantizer.modules[node.target]
|
|
module.activation_post_process = activation_post_process
|
|
quantized_module_cls = get_static_quant_module_class(
|
|
type(module), additional_static_quant_mapping)
|
|
quantized_module = quantized_module_cls.from_float(module)
|
|
parent_name, name = _parent_name(node.target)
|
|
setattr(quantizer.modules[parent_name], name, quantized_module)
|
|
return quantizer.quantized_graph.create_node(
|
|
'call_module',
|
|
node.target,
|
|
load_arg(quantized=[0])(node.args),
|
|
load_arg(quantized=False)(node.kwargs))
|
|
else:
|
|
assert node.op == "call_function"
|
|
# call_function
|
|
scale, zero_point = activation_post_process.calculate_qparams()
|
|
scale = float(scale)
|
|
zero_point = int(zero_point)
|
|
|
|
scale_arg, zero_point_arg = create_qparam_nodes(quantizer, node.name, scale, zero_point)
|
|
|
|
assert not isinstance(node.target, str), "Expecting node.target for "
|
|
"call_function to be a function instead of a string"
|
|
quantized_op = get_quantized_operator(node.target)
|
|
args = load_arg(quantized=[0])(node.args)
|
|
kwargs = {**load_arg(quantized=False)(node.kwargs), "output_scale": scale_arg,
|
|
"output_zero_point": zero_point_arg}
|
|
if quantized_op in ARGS_TO_SKIP:
|
|
args_to_skip = ARGS_TO_SKIP[quantized_op]
|
|
for arg in args_to_skip:
|
|
if arg in kwargs:
|
|
kwargs.pop(arg)
|
|
return quantizer.quantized_graph.create_node(
|
|
"call_function", quantized_op, args, kwargs)
|
|
else:
|
|
assert dtypes in [(torch.float16, torch.float16, None)]
|
|
# Generally fp16 kernels don't exist for fp16 ops
|
|
warnings.warn(
|
|
"Only reference patterns are currently supported for {dtype} dtype with {op} op"
|
|
"".format(dtype=dtypes, op=self.op))
|
|
return quantizer.quantized_graph.node_copy(node, load_arg(quantized=False))
|
|
else:
|
|
assert is_reference
|
|
if dtypes in [(torch.quint8, torch.qint8, None)]:
|
|
load_arg(quantized=[0])(node.args)
|
|
args = load_arg(quantized=False)(node.args)
|
|
kwargs = load_arg(quantized=False)(node.kwargs)
|
|
op_out = quantizer.quantized_graph.node_copy(node, load_arg(quantized=False))
|
|
cur_idx = quantizer.activation_post_process_indexes[node.name]
|
|
activation_post_process = \
|
|
quantizer.modules[quantizer.activation_post_process_map[node.name][cur_idx]]
|
|
quantizer.activation_post_process_indexes[node.name] += 1
|
|
return quantize_node(
|
|
quantizer, op_out, activation_post_process,
|
|
node, is_input=False)
|
|
else:
|
|
assert dtypes in [(torch.float16, torch.float16, None)]
|
|
return quantizer.quantized_graph.node_copy(node, load_arg(quantized=False))
|
|
|
|
|
|
# TODO: elu is using scale/zero_point instead of output_scale, output_zero_point
|
|
@register_quant_pattern(torch.nn.functional.elu)
|
|
class ELUQuantizeHandler(QuantizeHandler):
|
|
def convert(self, quantizer: QuantizerCls, node: Node, load_arg: Callable,
|
|
is_reference: bool = False,
|
|
convert_custom_config_dict: Dict[str, Any] = None) -> Node:
|
|
cur_idx = quantizer.activation_post_process_indexes[node.name]
|
|
activation_post_process = \
|
|
quantizer.modules[quantizer.activation_post_process_map[node.name][cur_idx]]
|
|
quantizer.activation_post_process_indexes[node.name] += 1
|
|
scale, zero_point = activation_post_process.calculate_qparams()
|
|
scale = float(scale)
|
|
zero_point = int(zero_point)
|
|
|
|
scale_arg, zero_point_arg = create_qparam_nodes(quantizer, node.name, scale, zero_point)
|
|
|
|
quantized_op = get_quantized_operator(node.target)
|
|
args = load_arg(quantized=[0])(node.args)
|
|
kwargs = {**load_arg(quantized=False)(node.kwargs), 'output_scale': scale_arg, 'output_zero_point': zero_point_arg}
|
|
kwargs.pop('inplace')
|
|
return quantizer.quantized_graph.create_node(
|
|
'call_function', quantized_op, args, kwargs)
|
|
|
|
@register_quant_pattern(torch.nn.Hardsigmoid, default_affine_fixed_qparams_fake_quant)
|
|
@register_quant_pattern(torch.nn.functional.hardsigmoid, default_affine_fixed_qparams_fake_quant)
|
|
@register_quant_pattern('hardsigmoid', default_affine_fixed_qparams_fake_quant)
|
|
@register_quant_pattern('hardsigmoid_', default_affine_fixed_qparams_fake_quant)
|
|
@register_quant_pattern(torch.nn.Sigmoid, default_affine_fixed_qparams_fake_quant)
|
|
@register_quant_pattern(torch.sigmoid, default_affine_fixed_qparams_fake_quant)
|
|
@register_quant_pattern('sigmoid', default_affine_fixed_qparams_fake_quant)
|
|
@register_quant_pattern('sigmoid_', default_affine_fixed_qparams_fake_quant)
|
|
@register_quant_pattern(torch.nn.Tanh, default_symmetric_fixed_qparams_fake_quant)
|
|
@register_quant_pattern(torch.tanh, default_symmetric_fixed_qparams_fake_quant)
|
|
@register_quant_pattern('tanh', default_symmetric_fixed_qparams_fake_quant)
|
|
@register_quant_pattern('tanh_', default_symmetric_fixed_qparams_fake_quant)
|
|
class FixedQParamsOpQuantizeHandler(QuantizeHandler):
|
|
def convert(self, quantizer: QuantizerCls, node: Node, load_arg: Callable,
|
|
is_reference: bool = False,
|
|
convert_custom_config_dict: Dict[str, Any] = None) -> Node:
|
|
qconfig = quantizer.qconfig_map[node.name]
|
|
dtypes = get_qconfig_dtypes(qconfig)
|
|
if dtypes == (torch.float16, torch.float16, None):
|
|
return quantizer.quantized_graph.node_copy(node, load_arg(quantized=False))
|
|
else:
|
|
return quantizer.quantized_graph.node_copy(node, load_arg(quantized=None))
|
|
|
|
|
|
# these ops have quantized equivalents that do not need any extra information
|
|
@register_quant_pattern(torch.nn.AdaptiveAvgPool1d)
|
|
@register_quant_pattern(torch.nn.AdaptiveAvgPool2d)
|
|
@register_quant_pattern(torch.nn.AdaptiveAvgPool3d)
|
|
@register_quant_pattern(torch.nn.AvgPool1d)
|
|
@register_quant_pattern(torch.nn.AvgPool2d)
|
|
@register_quant_pattern(torch.nn.AvgPool3d)
|
|
@register_quant_pattern(torch.nn.Dropout)
|
|
@register_quant_pattern(torch.nn.Hardtanh)
|
|
@register_quant_pattern(torch.nn.Identity)
|
|
@register_quant_pattern(torch.nn.MaxPool1d)
|
|
@register_quant_pattern(torch.nn.MaxPool2d)
|
|
@register_quant_pattern(torch.nn.MaxPool3d)
|
|
@register_quant_pattern(torch.nn.ReLU)
|
|
@register_quant_pattern(torch.nn.ReLU6)
|
|
@register_quant_pattern(torch.adaptive_avg_pool1d)
|
|
@register_quant_pattern(torch.nn.functional.adaptive_avg_pool2d)
|
|
@register_quant_pattern(torch.nn.functional.adaptive_avg_pool3d)
|
|
@register_quant_pattern(torch.nn.functional.dropout)
|
|
@register_quant_pattern(torch.nn.functional.hardtanh)
|
|
@register_quant_pattern(torch.nn.functional.hardtanh_)
|
|
@register_quant_pattern(torch.nn.functional.interpolate)
|
|
@register_quant_pattern(torch.nn.functional.max_pool1d)
|
|
@register_quant_pattern(torch.nn.functional.max_pool2d)
|
|
@register_quant_pattern(torch.nn.functional.max_pool3d)
|
|
@register_quant_pattern(torch.nn.functional.relu)
|
|
@register_quant_pattern(torch.nn.functional.relu6)
|
|
@register_quant_pattern(torch.avg_pool1d)
|
|
@register_quant_pattern(torch._C._nn.avg_pool2d)
|
|
@register_quant_pattern(torch._C._nn.avg_pool3d)
|
|
@register_quant_pattern(torch.chunk)
|
|
@register_quant_pattern(torch.clamp)
|
|
@register_quant_pattern(torch.flatten)
|
|
@register_quant_pattern(torch.transpose)
|
|
@register_quant_pattern(torch.max)
|
|
@register_quant_pattern(torch.mean)
|
|
@register_quant_pattern(torch.min)
|
|
@register_quant_pattern(torch.repeat_interleave)
|
|
@register_quant_pattern(torch.sort)
|
|
@register_quant_pattern(torch.squeeze)
|
|
@register_quant_pattern(torch.stack)
|
|
@register_quant_pattern(torch.unsqueeze)
|
|
@register_quant_pattern(operator.floordiv)
|
|
@register_quant_pattern(operator.getitem)
|
|
@register_quant_pattern('chunk')
|
|
@register_quant_pattern('clamp')
|
|
@register_quant_pattern('contiguous')
|
|
@register_quant_pattern('detach')
|
|
@register_quant_pattern('detach_')
|
|
@register_quant_pattern('mean')
|
|
@register_quant_pattern('numel')
|
|
@register_quant_pattern('permute')
|
|
@register_quant_pattern('relu')
|
|
@register_quant_pattern('relu_')
|
|
@register_quant_pattern('repeat')
|
|
@register_quant_pattern('repeat_interleave')
|
|
@register_quant_pattern('reshape')
|
|
@register_quant_pattern('resize_')
|
|
@register_quant_pattern('shape')
|
|
@register_quant_pattern('size')
|
|
@register_quant_pattern('squeeze')
|
|
@register_quant_pattern('squeeze_')
|
|
@register_quant_pattern('transpose')
|
|
@register_quant_pattern('unsqueeze')
|
|
@register_quant_pattern('unsqueeze_')
|
|
@register_quant_pattern('view')
|
|
class CopyNodeQuantizeHandler(QuantizeHandler):
|
|
def convert(self, quantizer: QuantizerCls, node: Node, load_arg: Callable,
|
|
is_reference: bool = False,
|
|
convert_custom_config_dict: Dict[str, Any] = None) -> Node:
|
|
return quantizer.quantized_graph.node_copy(node, load_arg(quantized=None))
|
|
|
|
# Default quantization handler, used for quantization of input and output
|
|
# of quantizable objects (e.g. modules and functionals)
|
|
class DefaultQuantizeHandler(QuantizeHandler):
|
|
def convert(self, quantizer: QuantizerCls, node: Node, load_arg: Callable,
|
|
is_reference: bool = False,
|
|
convert_custom_config_dict: Dict[str, Any] = None) -> Node:
|
|
assert self.all_node_args_are_tensors
|
|
root_module = quantizer.modules['']
|
|
cur_idx = quantizer.activation_post_process_indexes[node.name]
|
|
activation_post_process = \
|
|
quantizer.modules[quantizer.activation_post_process_map[node.name][cur_idx]]
|
|
quantizer.activation_post_process_indexes[node.name] += 1
|
|
return quantize_node(
|
|
quantizer,
|
|
node, activation_post_process, node, is_input=False)
|
|
|
|
class CustomModuleQuantizeHandler(QuantizeHandler):
|
|
def convert(self, quantizer: QuantizerCls, node: Node, load_arg: Callable,
|
|
is_reference: bool = False,
|
|
convert_custom_config_dict: Dict[str, Any] = None) -> Node:
|
|
""" Convert a float custom module to quantized custom module
|
|
"""
|
|
assert node.op == 'call_module'
|
|
assert convert_custom_config_dict is not None
|
|
custom_module_class_mapping = convert_custom_config_dict.get("observed_to_quantized_custom_module_class", None)
|
|
assert custom_module_class_mapping is not None
|
|
qconfig = quantizer.qconfig_map[node.name]
|
|
observed_custom_module = quantizer.modules[node.target]
|
|
if activation_is_statically_quantized(qconfig):
|
|
assert node.name in quantizer.activation_post_process_map
|
|
cur_idx = quantizer.activation_post_process_indexes[node.name]
|
|
observed_custom_module.activation_post_process = \
|
|
quantizer.modules[quantizer.activation_post_process_map[node.name][cur_idx]]
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|
quantizer.activation_post_process_indexes[node.name] += 1
|
|
quantized_custom_module_class = get_swapped_custom_module_class(
|
|
observed_custom_module, custom_module_class_mapping, qconfig)
|
|
quantized_custom_module = \
|
|
quantized_custom_module_class.from_observed(observed_custom_module)
|
|
parent_name, name = _parent_name(node.target)
|
|
setattr(quantizer.modules[parent_name], name, quantized_custom_module)
|
|
# hardcoded the qunatized input to be None (take whatever is in the environemnt),
|
|
# we can extend this
|
|
# if there is a need, e.g. get the indexes of quantized inputs from some
|
|
# module attribute like module._QUANTIZED_INPUT_INDEXES
|
|
return quantizer.quantized_graph.node_copy(node, load_arg(quantized=None))
|
|
|
|
class StandaloneModuleQuantizeHandler(QuantizeHandler):
|
|
""" Converts an observed standalone module to quantized standalone module
|
|
by calling convert_fx on the observed standalone module.
|
|
"""
|
|
def convert(self, quantizer: QuantizerCls, node: Node, load_arg: Callable,
|
|
is_reference: bool = False,
|
|
convert_custom_config_dict: Dict[str, Any] = None) -> Node:
|
|
assert node.op == 'call_module'
|
|
qconfig = quantizer.qconfig_map[node.name]
|
|
convert = torch.quantization.quantize_fx._convert_standalone_module_fx # type: ignore
|
|
observed_standalone_module = quantizer.modules[node.target]
|
|
input_quantized_idxs = observed_standalone_module._standalone_module_input_quantized_idxs.tolist()
|
|
quantized_standalone_module = convert(observed_standalone_module, is_reference=is_reference)
|
|
parent_name, name = _parent_name(node.target)
|
|
# update the modules dict
|
|
setattr(quantizer.modules[parent_name], name, quantized_standalone_module)
|
|
quantizer.modules[node.target] = quantized_standalone_module
|
|
return quantizer.quantized_graph.node_copy(node, load_arg(quantized=input_quantized_idxs))
|