# mypy: allow-untyped-defs import copy import operator from collections import namedtuple from collections.abc import Callable import torch import torch.ao.nn.intrinsic as nni import torch.ao.nn.intrinsic.qat as nniqat import torch.ao.nn.qat as nnqat import torch.ao.nn.quantized.reference as nnqr import torch.nn as nn import torch.nn.functional as F from torch.ao.quantization.fuser_method_mappings import ( _sequential_wrapper2, fuse_conv_bn, fuse_conv_bn_relu, fuse_convtranspose_bn, fuse_linear_bn, ) from .backend_config import ( BackendPatternConfig, DTypeConfig, DTypeWithConstraints, ObservationType, ) __all__: list[str] = [] # TODO: rename to be more explicit, e.g. qat_conv_relu _ConvMetadata = namedtuple( "_ConvMetadata", [ "root", "transpose", "bn", "reference", "transpose_reference", "fused_conv_relu", "fused_conv_bn", "fused_conv_bn_relu", "qat", "relu_qat", "bn_qat", "bn_relu_qat", "func", "func_transpose", ], ) _Conv1dMetadata = _ConvMetadata( nn.Conv1d, nn.ConvTranspose1d, nn.BatchNorm1d, nnqr.Conv1d, nnqr.ConvTranspose1d, nni.ConvReLU1d, nni.ConvBn1d, nni.ConvBnReLU1d, nnqat.Conv1d, nniqat.ConvReLU1d, nniqat.ConvBn1d, nniqat.ConvBnReLU1d, F.conv1d, F.conv_transpose1d, ) _Conv2dMetadata = _ConvMetadata( nn.Conv2d, nn.ConvTranspose2d, nn.BatchNorm2d, nnqr.Conv2d, nnqr.ConvTranspose2d, nni.ConvReLU2d, nni.ConvBn2d, nni.ConvBnReLU2d, nnqat.Conv2d, nniqat.ConvReLU2d, nniqat.ConvBn2d, nniqat.ConvBnReLU2d, F.conv2d, F.conv_transpose2d, ) _Conv3dMetadata = _ConvMetadata( nn.Conv3d, nn.ConvTranspose3d, nn.BatchNorm3d, nnqr.Conv3d, nnqr.ConvTranspose3d, nni.ConvReLU3d, nni.ConvBn3d, nni.ConvBnReLU3d, nnqat.Conv3d, nniqat.ConvReLU3d, nniqat.ConvBn3d, nniqat.ConvBnReLU3d, F.conv3d, F.conv_transpose3d, ) # Add constraints for fixed qparams ops like sigmoid and tanh to ensure values # fall within the proper ranges, e.g. [0, 1] for sigmoid, [-1, 1] for tanh _FIXED_QPARAM_OP_0TO1_CONSTRAINTS = DTypeWithConstraints( dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_exact_match=1.0 / 256.0, zero_point_exact_match=0, ) _FIXED_QPARAM_OP_NEG1TO1_CONSTRAINTS = DTypeWithConstraints( dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_exact_match=2.0 / 256.0, zero_point_exact_match=128, ) _FIXED_QPARAMS_OP_TO_CONSTRAINTS: dict[Callable | str, DTypeWithConstraints] = { torch.nn.Hardsigmoid: _FIXED_QPARAM_OP_0TO1_CONSTRAINTS, torch.nn.functional.hardsigmoid: _FIXED_QPARAM_OP_0TO1_CONSTRAINTS, "hardsigmoid": _FIXED_QPARAM_OP_0TO1_CONSTRAINTS, "hardsigmoid_": _FIXED_QPARAM_OP_0TO1_CONSTRAINTS, torch.nn.Sigmoid: _FIXED_QPARAM_OP_0TO1_CONSTRAINTS, torch.sigmoid: _FIXED_QPARAM_OP_0TO1_CONSTRAINTS, "sigmoid": _FIXED_QPARAM_OP_0TO1_CONSTRAINTS, "sigmoid_": _FIXED_QPARAM_OP_0TO1_CONSTRAINTS, torch.nn.Softmax: _FIXED_QPARAM_OP_0TO1_CONSTRAINTS, torch.nn.Tanh: _FIXED_QPARAM_OP_NEG1TO1_CONSTRAINTS, torch.tanh: _FIXED_QPARAM_OP_NEG1TO1_CONSTRAINTS, "tanh": _FIXED_QPARAM_OP_NEG1TO1_CONSTRAINTS, "tanh_": _FIXED_QPARAM_OP_NEG1TO1_CONSTRAINTS, } def _get_binary_op_configs( dtype_configs: list[DTypeConfig], ) -> list[BackendPatternConfig]: binary_op_configs: list[BackendPatternConfig] = [] num_tensor_args_to_observation_type_mapping = { # TODO: this is not used right now since we have extra check in prepare # will need to change this to NO_OBSERVER later after we implemented # Tensor dtype inference properly 0: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT, 1: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT, 2: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT, } for op_with_quantized_bop_scalar_variant in [ operator.add, torch.add, operator.mul, torch.mul, ]: bop_patterns = [ (op_with_quantized_bop_scalar_variant, nn.ReLU), (op_with_quantized_bop_scalar_variant, F.relu), (op_with_quantized_bop_scalar_variant, torch.relu), op_with_quantized_bop_scalar_variant, ] binary_op_configs.extend( BackendPatternConfig(bop_pattern) .set_dtype_configs(dtype_configs) # noqa: E131 ._set_num_tensor_args_to_observation_type( num_tensor_args_to_observation_type_mapping ) for bop_pattern in bop_patterns ) # matmul binary_op_configs.append( BackendPatternConfig(torch.matmul).set_dtype_configs(dtype_configs) # noqa: E131 ) return binary_op_configs def _get_linear_configs(dtype_configs: list[DTypeConfig]) -> list[BackendPatternConfig]: """ Return all configs related to linear modules and ops. """ observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT linear_configs: list[BackendPatternConfig] = [] # (1) Single linear modules/functions # ------------------------------------- # linear module linear_configs.append( BackendPatternConfig(torch.nn.Linear) .set_observation_type(observation_type) # noqa: E131 .set_dtype_configs(dtype_configs) .set_root_module(torch.nn.Linear) .set_reference_quantized_module(nnqr.Linear) .set_qat_module(nnqat.Linear) ) # linear qat module linear_configs.append( BackendPatternConfig(nnqat.Linear) .set_observation_type(observation_type) # noqa: E131 .set_dtype_configs(dtype_configs) .set_root_module(torch.nn.Linear) .set_reference_quantized_module(nnqr.Linear) ) # functional linear linear_configs.append( BackendPatternConfig(torch.nn.functional.linear) .set_observation_type(observation_type) # noqa: E131 .set_dtype_configs(dtype_configs) ._set_input_type_to_index({"weight": 1, "bias": 2}) ) # (2) Linear + relu # ------------------- # 2.1 linear module + relu fusion config # linear relu, linear module + relu module linear_configs.append( BackendPatternConfig((torch.nn.Linear, torch.nn.ReLU)) .set_dtype_configs(dtype_configs) # noqa: E131 .set_fuser_method(_sequential_wrapper2(nni.LinearReLU)) .set_fused_module(nni.LinearReLU) ) # linear relu, linear module + functional relu linear_configs.append( BackendPatternConfig((torch.nn.Linear, torch.nn.functional.relu)) .set_dtype_configs(dtype_configs) # noqa: E131 .set_fuser_method(_sequential_wrapper2(nni.LinearReLU)) .set_fused_module(nni.LinearReLU) ) # 2.2 linear module + relu, fused module configs # linear relu, fused module linear_configs.append( BackendPatternConfig(nni.LinearReLU) .set_observation_type(observation_type) # noqa: E131 .set_dtype_configs(dtype_configs) .set_root_module(torch.nn.Linear) .set_reference_quantized_module(nnqr.Linear) .set_qat_module(nniqat.LinearReLU) ) # linear relu, qat fused module linear_configs.append( BackendPatternConfig(nniqat.LinearReLU) .set_observation_type(observation_type) # noqa: E131 .set_dtype_configs(dtype_configs) .set_root_module(torch.nn.Linear) .set_reference_quantized_module(nnqr.Linear) ) # 2.3 functional linear + relu configs # linear relu, functional linear + relu module linear_configs.append( BackendPatternConfig((F.linear, torch.nn.ReLU)) .set_observation_type(observation_type) # noqa: E131 .set_dtype_configs(dtype_configs) ) # linear relu, functional linear + functional relu linear_configs.append( BackendPatternConfig((F.linear, F.relu)) .set_observation_type(observation_type) # noqa: E131 .set_dtype_configs(dtype_configs) ) # (3) Linear + batchnorm # ------------------------ # 3.1 linear bn fusion linear_configs.append( BackendPatternConfig((nn.Linear, nn.BatchNorm1d)) .set_dtype_configs(dtype_configs) # noqa: E131 .set_fuser_method(fuse_linear_bn) .set_fused_module(nni.LinearBn1d) ) # 3.2 linear bn fused # linear bn, fused module linear_configs.append( BackendPatternConfig(nni.LinearBn1d) .set_observation_type(observation_type) # noqa: E131 .set_dtype_configs(dtype_configs) .set_root_module(torch.nn.Linear) .set_reference_quantized_module(nnqr.Linear) .set_qat_module(nniqat.LinearBn1d) ) # linear bn, qat fused module linear_configs.append( BackendPatternConfig(nniqat.LinearBn1d) .set_observation_type(observation_type) # noqa: E131 .set_dtype_configs(dtype_configs) .set_root_module(torch.nn.Linear) .set_reference_quantized_module(nnqr.Linear) ) return linear_configs def _get_conv_configs(dtype_configs): """ Return all configs related to conv modules and ops. """ conv_configs = [] observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT for convs in [_Conv1dMetadata, _Conv2dMetadata, _Conv3dMetadata]: # (1) Single conv modules/functions # ----------------------------------- # conv module conv_configs.append( BackendPatternConfig(convs.root) .set_observation_type(observation_type) # noqa: E131 .set_dtype_configs(dtype_configs) .set_root_module(convs.root) .set_reference_quantized_module(convs.reference) .set_qat_module(convs.qat) ) # conv qat module conv_configs.append( BackendPatternConfig(convs.qat) .set_observation_type(observation_type) # noqa: E131 .set_dtype_configs(dtype_configs) .set_root_module(convs.root) .set_reference_quantized_module(convs.reference) ) # functional conv conv_configs.append( BackendPatternConfig(convs.func) .set_observation_type(observation_type) # noqa: E131 .set_dtype_configs(dtype_configs) ._set_input_type_to_index({"weight": 1, "bias": 2}) ) # (2) Conv + relu # ----------------- # 2.1 conv module + relu fusion configs # conv relu fusion, conv module + relu module conv_configs.append( BackendPatternConfig((convs.root, torch.nn.ReLU)) .set_dtype_configs(dtype_configs) # noqa: E131 .set_fuser_method(_sequential_wrapper2(convs.fused_conv_relu)) .set_fused_module(convs.fused_conv_relu) ) # conv relu fusion, conv module + functional relu conv_configs.append( BackendPatternConfig((convs.root, F.relu)) .set_dtype_configs(dtype_configs) # noqa: E131 .set_fuser_method(_sequential_wrapper2(convs.fused_conv_relu)) .set_fused_module(convs.fused_conv_relu) ) # 2.2 conv module + relu fused module configs # conv relu, fused module conv_configs.append( BackendPatternConfig(convs.fused_conv_relu) .set_observation_type(observation_type) # noqa: E131 .set_dtype_configs(dtype_configs) .set_root_module(convs.root) .set_reference_quantized_module(convs.reference) .set_qat_module(convs.relu_qat) ) # conv relu, qat fused module conv_configs.append( BackendPatternConfig(convs.relu_qat) .set_observation_type(observation_type) # noqa: E131 .set_dtype_configs(dtype_configs) .set_root_module(convs.root) .set_reference_quantized_module(convs.reference) ) # 2.3 functional conv + relu configs # conv relu, functional conv + relu module conv_configs.append( BackendPatternConfig((convs.func, torch.nn.ReLU)) .set_observation_type(observation_type) # noqa: E131 .set_dtype_configs(dtype_configs) ) # conv relu, functional conv + functional relu conv_configs.append( BackendPatternConfig((convs.func, F.relu)) .set_observation_type(observation_type) # noqa: E131 .set_dtype_configs(dtype_configs) ) # fused conv relu conv_configs.append( BackendPatternConfig(convs.fused_conv_relu) .set_dtype_configs(dtype_configs) # noqa: E131 .set_qat_module(convs.relu_qat) ) conv_configs.append( BackendPatternConfig(convs.relu_qat) .set_dtype_configs(dtype_configs) # noqa: E131 .set_root_module(convs.root) .set_reference_quantized_module(convs.reference) ) # (3) Conv + batchnorm (+ relu) # ------------------------------- # 3.1 conv bn fusion configs # conv + bn fusion conv_configs.append( BackendPatternConfig((convs.root, convs.bn)) .set_dtype_configs(dtype_configs) # noqa: E131 .set_fuser_method(fuse_conv_bn) .set_fused_module(convs.fused_conv_bn) ) # conv + bn + relu module fusion conv_configs.append( BackendPatternConfig((convs.root, convs.bn, nn.ReLU)) .set_dtype_configs(dtype_configs) # noqa: E131 .set_fuser_method(fuse_conv_bn_relu) .set_fused_module(convs.fused_conv_bn_relu) ) # conv + bn + relu functional fusion conv_configs.append( BackendPatternConfig((convs.root, convs.bn, F.relu)) .set_dtype_configs(dtype_configs) # noqa: E131 .set_root_module(convs.root) .set_fuser_method(fuse_conv_bn_relu) .set_fused_module(convs.fused_conv_bn_relu) ) # TODO: we can add fusion for torch.relu as well # 3.2 conv + bn (+ relu) fused module configs # fused conv bn conv_configs.append( BackendPatternConfig(convs.fused_conv_bn) .set_dtype_configs(dtype_configs) # noqa: E131 .set_qat_module(convs.bn_qat) ) # fused conv bn relu conv_configs.append( BackendPatternConfig(convs.fused_conv_bn_relu) .set_dtype_configs(dtype_configs) # noqa: E131 .set_qat_module(convs.bn_relu_qat) ) # conv bn, qat fused module conv_configs.append( BackendPatternConfig(convs.bn_qat) .set_observation_type(observation_type) # noqa: E131 .set_dtype_configs(dtype_configs) .set_root_module(convs.root) .set_reference_quantized_module(convs.reference) ) # conv bn relu, qat fused module conv_configs.append( BackendPatternConfig(convs.bn_relu_qat) .set_observation_type(observation_type) # noqa: E131 .set_dtype_configs(dtype_configs) .set_root_module(convs.root) .set_reference_quantized_module(convs.reference) ) # (4) conv transpose and its fusion # 4.1 conv transpose config conv_configs.append( BackendPatternConfig(convs.transpose) .set_dtype_configs(dtype_configs) # noqa: E131 .set_root_module(convs.transpose) .set_reference_quantized_module(convs.transpose_reference) ) # 4.2 conv transpose + bn fusion conv_configs.append( BackendPatternConfig((convs.transpose, convs.bn)) .set_dtype_configs(dtype_configs) # noqa: E131 .set_fuser_method(fuse_convtranspose_bn) .set_root_module(convs.transpose) .set_reference_quantized_module(convs.transpose_reference) ) # 4.3 functional conv transpose conv_configs.append( BackendPatternConfig(convs.func_transpose) .set_dtype_configs(dtype_configs) # noqa: E131 ._set_input_type_to_index({"weight": 1, "bias": 2}) ) return conv_configs def _get_cat_config(dtype_configs: list[DTypeConfig]) -> BackendPatternConfig: return ( BackendPatternConfig(torch.cat) .set_observation_type(ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT) .set_dtype_configs(dtype_configs) ) def _get_ln_configs(dtype_configs: list[DTypeConfig]) -> list[BackendPatternConfig]: ln_configs = [] ln_configs.append( BackendPatternConfig(torch.nn.LayerNorm) .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT) # noqa: E131 .set_dtype_configs(dtype_configs) ) ln_configs.append( BackendPatternConfig(torch.nn.functional.layer_norm) .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT) # noqa: E131 .set_dtype_configs(dtype_configs) ._set_input_type_to_index({"weight": 2, "bias": 3}) ) return ln_configs def _get_default_op_configs( dtype_configs: list[DTypeConfig], ) -> list[BackendPatternConfig]: default_ops = [ torch.nn.ELU, torch.nn.LeakyReLU, torch.nn.Hardswish, torch.nn.InstanceNorm1d, torch.nn.InstanceNorm2d, torch.nn.InstanceNorm3d, torch.nn.Dropout, torch.nn.PReLU, torch.nn.functional.elu, torch.nn.functional.hardswish, torch.nn.functional.leaky_relu, torch.nn.functional.dropout, ] configs = [ BackendPatternConfig(op) .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT) # noqa: E131 .set_dtype_configs(dtype_configs) for op in default_ops ] configs.append( BackendPatternConfig(torch.nn.functional.group_norm) .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT) # noqa: E131 .set_dtype_configs(dtype_configs) ._set_input_type_to_index({"weight": 2, "bias": 3}) ) configs.append( BackendPatternConfig(torch.nn.functional.instance_norm) .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT) # noqa: E131 .set_dtype_configs(dtype_configs) ._set_input_type_to_index({"weight": 3, "bias": 4}) ) return configs def _add_fixed_qparams_to_dtype_configs( dtype_configs: list[DTypeConfig], constraints: DTypeWithConstraints, ) -> list[DTypeConfig]: """ Return a copy of the list of DTypeConfigs where activations are subject to the specified constraints required for fixed qparams ops. If the data type doesn't match the one in the constraints, simply leave the corresponding DTypeConfig unchanged. If `scale_min_lower_bound` or `scale_max_upper_bound` is specified in the activations, throw an exception since these settings are incompatible with fixed qparams ops. """ new_dtype_configs = [] for dtype_config in dtype_configs: dc = copy.deepcopy(dtype_config) for orig_constraints in [ dc.input_dtype_with_constraints, dc.output_dtype_with_constraints, ]: if orig_constraints.dtype != constraints.dtype: continue if orig_constraints.scale_min_lower_bound is not None: raise ValueError( f"scale_min_lower_bound is invalid for fixed qparams ops: {dtype_config}" ) if orig_constraints.scale_max_upper_bound is not None: raise ValueError( f"scale_max_upper_bound is invalid for fixed qparams ops: {dtype_config}" ) orig_constraints.quant_min_lower_bound = constraints.quant_min_lower_bound orig_constraints.quant_max_upper_bound = constraints.quant_max_upper_bound orig_constraints.scale_exact_match = constraints.scale_exact_match orig_constraints.zero_point_exact_match = constraints.zero_point_exact_match new_dtype_configs.append(dc) return new_dtype_configs def _get_fixed_qparams_op_configs( dtype_configs: list[DTypeConfig], ) -> list[BackendPatternConfig]: fixed_qparams_op_configs = [] for fixed_qparam_op, constraints in _FIXED_QPARAMS_OP_TO_CONSTRAINTS.items(): new_dtype_configs = _add_fixed_qparams_to_dtype_configs( dtype_configs, constraints ) fixed_qparams_op_configs.append( BackendPatternConfig(fixed_qparam_op) .set_observation_type( ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT ) # noqa: E131 .set_dtype_configs(new_dtype_configs) ) return fixed_qparams_op_configs def _get_share_qparams_op_configs(dtype_configs): """Get the operator config for the operators that works for both float and quantized input if input is quantized, the output Tensor shares the same quantization parameter with input. Example operator: avgpool2d, reshape, transpose, maxpool2d Example observed operator: observer_0 - avgpool2d - observer_0 (same observer instance as input) """ def _get_share_qprams_op_backend_config(op): return ( BackendPatternConfig(op) .set_observation_type(ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT) .set_dtype_configs(dtype_configs) ) share_qparams_ops = [ torch.nn.AdaptiveAvgPool1d, torch.nn.AdaptiveAvgPool2d, torch.nn.AdaptiveAvgPool3d, torch.nn.AvgPool1d, torch.nn.AvgPool2d, torch.nn.AvgPool3d, torch.nn.Hardtanh, torch.nn.Identity, torch.nn.MaxPool1d, torch.nn.MaxPool2d, torch.nn.MaxPool3d, torch.nn.PixelShuffle, torch.nn.PixelUnshuffle, torch.nn.ReLU, torch.nn.ReLU6, torch.adaptive_avg_pool1d, torch.nn.functional.adaptive_avg_pool2d, torch.nn.functional.adaptive_avg_pool3d, torch.nn.functional.hardtanh, torch.nn.functional.hardtanh_, torch.nn.functional.interpolate, torch.nn.functional.max_pool1d, torch.nn.functional.max_pool2d, torch.nn.functional.max_pool3d, torch.nn.functional.pixel_shuffle, torch.nn.functional.pixel_unshuffle, torch.nn.functional.relu, torch.nn.functional.relu6, torch.avg_pool1d, torch._C._nn.avg_pool2d, torch._C._nn.avg_pool3d, torch.clamp, torch.flatten, torch.mean, torch.narrow, torch.repeat_interleave, torch.transpose, torch.squeeze, torch.stack, torch.unsqueeze, operator.floordiv, "contiguous", "clamp", "detach", "detach_", "mean", "permute", "repeat", "repeat_interleave", "reshape", "resize_", "relu", "relu_", "squeeze", "squeeze_", "transpose", "unsqueeze", "unsqueeze_", "view", ] return [_get_share_qprams_op_backend_config(op) for op in share_qparams_ops] def _get_bn_configs(dtype_configs: list[DTypeConfig]) -> list[BackendPatternConfig]: """Get configs related to batchnorm.""" bn_configs = [] bn_to_fused_bn = { torch.nn.BatchNorm2d: nni.BNReLU2d, torch.nn.BatchNorm3d: nni.BNReLU3d, } for bn in bn_to_fused_bn: fused_bn = bn_to_fused_bn[bn] # bn module + relu module fusion config bn_configs.append( BackendPatternConfig((bn, nn.ReLU)) .set_dtype_configs(dtype_configs) # noqa: E131 .set_fuser_method(_sequential_wrapper2(fused_bn)) .set_fused_module(fused_bn) ) # bn module + F.relu fusion config bn_configs.append( BackendPatternConfig((bn, F.relu)) .set_dtype_configs(dtype_configs) # noqa: E131 .set_fuser_method(_sequential_wrapper2(fused_bn)) .set_fused_module(fused_bn) ) bn_configs.append( BackendPatternConfig(bn) .set_observation_type( ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT ) # noqa: E131 .set_dtype_configs(dtype_configs) ) # fused bn configs for fused_bn in bn_to_fused_bn.values(): bn_configs.append( BackendPatternConfig(fused_bn) .set_observation_type( ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT ) # noqa: E131 .set_dtype_configs(dtype_configs) ) return bn_configs def _get_rnn_op_configs(dtype_configs: list[DTypeConfig]) -> list[BackendPatternConfig]: rnn_op_configs = [] for rnn_op, ref_rnn_op in [ (nn.GRUCell, nnqr.GRUCell), (nn.LSTMCell, nnqr.LSTMCell), (nn.RNNCell, nnqr.RNNCell), (nn.LSTM, nnqr.LSTM), (nn.GRU, nnqr.GRU), ]: rnn_op_configs.append( BackendPatternConfig(rnn_op) .set_observation_type( ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT ) # noqa: E131 .set_dtype_configs(dtype_configs) .set_root_module(rnn_op) .set_reference_quantized_module(ref_rnn_op) ) return rnn_op_configs def _get_embedding_op_configs( dtype_configs: list[DTypeConfig], ) -> list[BackendPatternConfig]: embedding_op_configs = [] for embedding_op, qat_embedding_op, ref_embedding_op in [ (nn.Embedding, nnqat.Embedding, nnqr.Embedding), (nn.EmbeddingBag, nnqat.EmbeddingBag, nnqr.EmbeddingBag), ]: embedding_op_configs.append( BackendPatternConfig(embedding_op) .set_observation_type( ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT ) # noqa: E131 .set_dtype_configs(dtype_configs) .set_qat_module(qat_embedding_op) .set_root_module(embedding_op) .set_reference_quantized_module(ref_embedding_op) ) # config for qat op embedding_op_configs.append( BackendPatternConfig(qat_embedding_op) .set_observation_type( ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT ) # noqa: E131 .set_dtype_configs(dtype_configs) .set_root_module(embedding_op) .set_reference_quantized_module(ref_embedding_op) ) return embedding_op_configs def _get_tensor_info_op_configs(dtype_configs): """ These ops work on tensors of different dtypes but return non-tensors containing information about the input tensor. """ def _get_config(op): return ( BackendPatternConfig(op) .set_observation_type(ObservationType.INPUT_OUTPUT_NOT_OBSERVED) .set_dtype_configs(dtype_configs) ) return [_get_config(op) for op in ("shape", "size")]