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# mypy: allow-untyped-defs
import torch
from ._common_operator_config_utils import (
_get_binary_op_configs,
_get_bn_configs,
_get_cat_config,
_get_conv_configs,
_get_default_op_configs,
_get_embedding_op_configs,
_get_fixed_qparams_op_configs,
_get_linear_configs,
_get_ln_configs,
_get_rnn_op_configs,
_get_share_qparams_op_configs,
_get_tensor_info_op_configs,
)
from .backend_config import BackendConfig, DTypeConfig
__all__ = [
"get_test_only_legacy_native_backend_config",
"default_op_quint8_dtype_config",
"default_op_fp16_dtype_config",
"default_dynamic_int8_dtype_config",
"default_dynamic_float16_dtype_config",
"input_output_only_quint8_dtype_config",
"weight_only_quint8_dtype_config",
"weight_only_quint4x2_dtype_config",
"get_native_backend_config",
"get_native_backend_config_dict",
"get_test_only_legacy_native_backend_config_dict",
]
# ===================
# | DTYPE CONFIGS |
# ===================
# weighted op int8 dtype config
# this is config for ops that has quantized weights, like linear, conv
weighted_op_quint8_dtype_config = DTypeConfig(
input_dtype=torch.quint8,
output_dtype=torch.quint8,
weight_dtype=torch.qint8,
bias_dtype=torch.float,
)
default_op_quint8_dtype_config = DTypeConfig(
input_dtype=torch.quint8,
output_dtype=torch.quint8,
)
default_op_fp16_dtype_config = DTypeConfig(
input_dtype=torch.float16,
output_dtype=torch.float16,
weight_dtype=torch.float16,
bias_dtype=torch.float16,
)
default_dynamic_int8_dtype_config = DTypeConfig(
input_dtype=torch.quint8,
output_dtype=torch.float,
weight_dtype=torch.qint8,
bias_dtype=torch.float,
# currently the dtype check is not yet enabled, so we provided the dtype_configs but
# it is not really used yet,
# we will enable it a bit later after we moved everything to backend_config_dict
is_dynamic=True,
)
default_dynamic_float16_dtype_config = DTypeConfig(
input_dtype=torch.float16,
output_dtype=torch.float,
weight_dtype=torch.float16,
bias_dtype=torch.float,
# currently the dtype check is not yet enabled, so we provided the dtype_configs but
# it is not really used yet,
# we will enable it a bit later after we moved everything to backend_config_dict
is_dynamic=True,
)
# Needed for LayerNorm and f.layer_norm, since currently the kernel only supports float weights
input_output_only_quint8_dtype_config = DTypeConfig(
input_dtype=torch.quint8,
output_dtype=torch.quint8,
weight_dtype=torch.float,
bias_dtype=torch.float,
)
weight_only_quint8_dtype_config = DTypeConfig(
input_dtype=torch.float,
output_dtype=torch.float,
weight_dtype=torch.quint8,
)
weight_only_quint4x2_dtype_config = DTypeConfig(
input_dtype=torch.float,
output_dtype=torch.float,
weight_dtype=torch.quint4x2,
)
# =====================
# | BACKEND CONFIGS |
# =====================
def get_test_only_legacy_native_backend_config() -> BackendConfig:
"""
Return the `BackendConfig` for PyTorch Native backend (fbgemm/qnnpack) with various additional fp16 ops.
"""
conv_dtype_configs = [weighted_op_quint8_dtype_config]
linear_dtype_configs = [
weighted_op_quint8_dtype_config,
default_dynamic_int8_dtype_config,
default_dynamic_float16_dtype_config,
default_op_fp16_dtype_config,
]
binary_op_dtype_configs = [
default_op_quint8_dtype_config,
default_op_fp16_dtype_config,
]
default_op_dtype_configs = [default_op_quint8_dtype_config]
fixed_qparams_op_dtype_configs = [
default_op_quint8_dtype_config,
default_op_fp16_dtype_config,
]
share_qparams_op_dtype_configs = [
default_op_quint8_dtype_config,
default_op_fp16_dtype_config,
]
tensor_info_op_dtype_configs = [
default_op_quint8_dtype_config,
]
rnn_op_dtype_configs = [
default_dynamic_int8_dtype_config,
default_dynamic_float16_dtype_config,
]
embedding_op_dtype_configs = [
weight_only_quint8_dtype_config,
weight_only_quint4x2_dtype_config,
]
layer_norm_op_dtype_configs = [input_output_only_quint8_dtype_config]
return (
BackendConfig("_native_and_fp16")
.set_backend_pattern_configs(_get_conv_configs(conv_dtype_configs))
.set_backend_pattern_configs(_get_linear_configs(linear_dtype_configs))
.set_backend_pattern_configs(_get_binary_op_configs(binary_op_dtype_configs))
.set_backend_pattern_config(_get_cat_config(default_op_dtype_configs))
.set_backend_pattern_configs(_get_default_op_configs(default_op_dtype_configs))
.set_backend_pattern_configs(
_get_fixed_qparams_op_configs(fixed_qparams_op_dtype_configs)
)
.set_backend_pattern_configs(
_get_share_qparams_op_configs(share_qparams_op_dtype_configs)
)
.set_backend_pattern_configs(
_get_tensor_info_op_configs(tensor_info_op_dtype_configs)
)
.set_backend_pattern_configs(_get_bn_configs(default_op_dtype_configs))
.set_backend_pattern_configs(_get_ln_configs(layer_norm_op_dtype_configs))
.set_backend_pattern_configs(_get_rnn_op_configs(rnn_op_dtype_configs))
.set_backend_pattern_configs(
_get_embedding_op_configs(embedding_op_dtype_configs)
)
)
def get_native_backend_config() -> BackendConfig:
"""
Return the `BackendConfig` for PyTorch Native backend (fbgemm/qnnpack).
"""
# TODO: express this BackendConfig as a union of the FBGEMM and QNNPACK BackendConfigs
conv_dtype_configs = [weighted_op_quint8_dtype_config]
linear_dtype_configs = [
weighted_op_quint8_dtype_config,
default_dynamic_int8_dtype_config,
default_dynamic_float16_dtype_config,
]
binary_op_dtype_configs = [default_op_quint8_dtype_config]
default_op_dtype_configs = [default_op_quint8_dtype_config]
fixed_qparams_op_dtype_configs = [default_op_quint8_dtype_config]
share_qparams_op_dtype_configs = [default_op_quint8_dtype_config]
tensor_info_op_dtype_configs = [default_op_quint8_dtype_config]
rnn_op_dtype_configs = [
default_dynamic_int8_dtype_config,
default_dynamic_float16_dtype_config,
]
embedding_op_dtype_configs = [
weight_only_quint8_dtype_config,
weight_only_quint4x2_dtype_config,
]
layer_norm_op_dtype_configs = [input_output_only_quint8_dtype_config]
return (
BackendConfig("native")
.set_backend_pattern_configs(_get_conv_configs(conv_dtype_configs))
.set_backend_pattern_configs(_get_linear_configs(linear_dtype_configs))
.set_backend_pattern_configs(_get_binary_op_configs(binary_op_dtype_configs))
.set_backend_pattern_config(_get_cat_config(default_op_dtype_configs))
.set_backend_pattern_configs(_get_default_op_configs(default_op_dtype_configs))
.set_backend_pattern_configs(
_get_fixed_qparams_op_configs(fixed_qparams_op_dtype_configs)
)
.set_backend_pattern_configs(
_get_share_qparams_op_configs(share_qparams_op_dtype_configs)
)
.set_backend_pattern_configs(
_get_tensor_info_op_configs(tensor_info_op_dtype_configs)
)
.set_backend_pattern_configs(_get_bn_configs(default_op_dtype_configs))
.set_backend_pattern_configs(_get_ln_configs(layer_norm_op_dtype_configs))
.set_backend_pattern_configs(_get_rnn_op_configs(rnn_op_dtype_configs))
.set_backend_pattern_configs(
_get_embedding_op_configs(embedding_op_dtype_configs)
)
)
def get_native_backend_config_dict():
"""
Return the `BackendConfig` for PyTorch Native backend (fbgemm/qnnpack) in dictionary form.
"""
return get_native_backend_config().to_dict()
def get_test_only_legacy_native_backend_config_dict():
"""
Return the `BackendConfig` for PyTorch Native backend (fbgemm/qnnpack) with various additional
fp16 ops in dictionary form.
"""
return get_test_only_legacy_native_backend_config().to_dict()
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