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Delete old preprocess code #2017

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cdb8957
Fix Lint (#2036)
mcr229 Feb 22, 2024
e0a022a
Delete old preprocess code (#2017)
mcr229 Feb 22, 2024
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6 changes: 5 additions & 1 deletion backends/qualcomm/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -70,7 +70,11 @@ if(CMAKE_BUILD_TYPE STREQUAL "Release")
set(CMAKE_CXX_VISIBILITY_PRESET hidden)

# --gc-sections is added by torch.
add_compile_options("-O3" "-ffunction-sections" "-fdata-sections" "-frtti" "-Wno-unused-command-line-argument")
add_compile_options("-O3"
"-ffunction-sections"
"-fdata-sections"
"-frtti"
"-Wno-unused-command-line-argument")
endif()

include_directories(
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115 changes: 5 additions & 110 deletions backends/xnnpack/xnnpack_preprocess.py
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Expand Up @@ -11,8 +11,6 @@
from typing import Dict, final, List

import torch

from executorch.backends.transforms import get_shape
from executorch.backends.xnnpack.operators.node_visitor import get_node_visitors

from executorch.backends.xnnpack.passes import XNNPACKPassManager
Expand All @@ -21,19 +19,18 @@

from executorch.backends.xnnpack.serialization.xnnpack_graph_schema import (
Buffer,
PerChannelQuant,
PerTensorQuant,
XNNDatatype,
XNNGraph,
XNNQuantizedTensorValue,
XNNTensorValue,
XValue,
)
from executorch.backends.xnnpack.serialization.xnnpack_graph_serialize import (
serialize_xnnpack_binary,
)
from executorch.backends.xnnpack.utils.utils import is_param_node

from executorch.backends.xnnpack.utils.xnnpack_constants import (
XNN_VALUE_FLAG_EXTERNAL_INPUT,
XNN_VALUE_FLAG_EXTERNAL_OUTPUT,
)

from executorch.exir.backend.backend_details import (
BackendDetails,
CompileSpec,
Expand All @@ -42,17 +39,6 @@
from executorch.exir.verification.verifier import EXIREdgeDialectVerifier
from torch.export.exported_program import ExportedProgram

XNN_VALUE_FLAG_NON_EXTERNAL = 0
XNN_VALUE_FLAG_EXTERNAL_INPUT = 1
XNN_VALUE_FLAG_EXTERNAL_OUTPUT = 2
XNN_FLAG_TRANSPOSE_WEIGHTS = 1
XNN_INVALID_VALUE_ID = 2**32 - 1
XNN_TYPE_MAP = {
torch.float32: XNNDatatype.xnn_datatype_fp32,
torch.uint8: XNNDatatype.xnn_datatype_quint8,
torch.int8: XNNDatatype.xnn_datatype_qint8,
torch.int32: XNNDatatype.xnn_datatype_qint32,
}
DEFAULT_DEBUG_HANDLE = 65535

logger = logging.getLogger(__name__)
Expand All @@ -65,97 +51,6 @@ class ExternalMeta:
io_type: int


def node_to_xvalue(
node: torch.fx.Node,
constant_buffer_idx: int,
external_id: int,
flags: int,
id_out: int,
dq_datatype=XNNDatatype.xnn_datatype_invalid,
) -> XValue:
node_val = node.meta["val"]
node_value = XValue(
xvalue_union=XNNTensorValue(
datatype=XNN_TYPE_MAP[node_val.dtype],
num_dims=node_val.dim(),
dims=get_shape(node),
constant_buffer_idx=constant_buffer_idx,
external_id=external_id,
flags=flags,
id_out=id_out,
dq_datatype=dq_datatype,
)
)
return node_value


def node_to_per_tensor_quantized_xvalue(
node: torch.fx.Node,
dtype: torch.dtype,
constant_buffer_idx: int,
external_id: int,
flags: int,
id_out: int,
scale: float,
zero_point: int,
) -> XValue:
node_val = node.meta["val"]
node_xvalue = XNNTensorValue(
datatype=XNN_TYPE_MAP[dtype],
num_dims=node_val.dim(),
dims=get_shape(node),
constant_buffer_idx=constant_buffer_idx,
external_id=external_id,
flags=flags,
id_out=id_out,
dq_datatype=XNNDatatype.xnn_datatype_invalid, # always invalid
)

per_tensor_quantized_params = PerTensorQuant(scale=scale, zero_point=zero_point)
quantized_node_val = XValue(
xvalue_union=XNNQuantizedTensorValue(
tensor_value=node_xvalue,
quant_params=per_tensor_quantized_params,
)
)
return quantized_node_val


def node_to_per_channel_quantized_xvalue(
node: torch.fx.Node,
dtype: torch.dtype,
constant_buffer_idx: int,
external_id: int,
flags: int,
id_out: int,
channel_dim: int,
scale: torch.Tensor,
) -> XValue:
node_val = node.meta["val"]
assert dtype == torch.torch.int8
node_xvalue = XNNTensorValue(
datatype=XNNDatatype.xnn_datatype_qcint8, # HACK: XNN_TYPE_MAP[dtype],
num_dims=node_val.dim(),
dims=get_shape(node),
constant_buffer_idx=constant_buffer_idx,
external_id=external_id,
flags=flags,
id_out=id_out,
dq_datatype=XNNDatatype.xnn_datatype_invalid, # always invalid
)

per_channel_quantized_params = PerChannelQuant(
scale=scale.tolist(), channel_dim=channel_dim
)
quantized_node_val = XValue(
xvalue_union=XNNQuantizedTensorValue(
tensor_value=node_xvalue,
quant_params=per_channel_quantized_params,
)
)
return quantized_node_val


def generate_node_to_external_map(
exported_program: ExportedProgram,
edge_graph_module: torch.fx.GraphModule,
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