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[QNN] Legalization for Intel x86 QNN Conv2D (#3896)
* QNNLegalize for conv2d * [QNN] Legalization for Intel x86 QNN Conv2D
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# Licensed to the Apache Software Foundation (ASF) under one | ||
# or more contributor license agreements. See the NOTICE file | ||
# distributed with this work for additional information | ||
# regarding copyright ownership. The ASF licenses this file | ||
# to you under the Apache License, Version 2.0 (the | ||
# "License"); you may not use this file except in compliance | ||
# with the License. You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, | ||
# software distributed under the License is distributed on an | ||
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
# KIND, either express or implied. See the License for the | ||
# specific language governing permissions and limitations | ||
# under the License. | ||
# pylint: disable=invalid-name, unused-argument | ||
"""Backend QNN related feature registration""" | ||
from __future__ import absolute_import | ||
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import tvm | ||
from tvm import relay | ||
from .. import op as reg | ||
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# Registering QNN Conv2D legalization function. | ||
@reg.register_qnn_legalize("qnn.conv2d") | ||
def legalize_qnn_conv2d(attrs, inputs, types): | ||
"""Legalizes QNN conv2d op. | ||
Parameters | ||
---------- | ||
attrs : tvm.attrs.Attrs | ||
Attributes of current convolution | ||
inputs : list of tvm.relay.Expr | ||
The args of the Relay expr to be legalized | ||
types : list of types | ||
List of input and output types | ||
Returns | ||
------- | ||
result : tvm.relay.Expr | ||
The legalized expr | ||
""" | ||
return qnn_conv2d_legalize(attrs, inputs, types) | ||
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# Generic QNN Conv2D legalization function. | ||
@tvm.target.generic_func | ||
def qnn_conv2d_legalize(attrs, inputs, types): | ||
"""Default legalization is None.""" | ||
return None | ||
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# Intel x86 QNN Conv2D legalization function. | ||
@qnn_conv2d_legalize.register('cpu') | ||
def _qnn_conv2d_legalize(attrs, inputs, types): | ||
"""Legalizes QNN conv2d op. VNNI supports u8 x i8 fast conv/MM. If the dtypes are already good, | ||
we dont transform. Else, we shift the tensor values and zero points to change the dtype. | ||
Converting from int8 to uint8 can be done in following manner. | ||
Original equation | ||
scale * (QA - zp_a) | ||
scale * (QA + 128 - 128 - zp_a) | ||
scale * ( (QA + 128) - (zp_a + 128)) | ||
Replacing QA + 128 with QA' and (zp_a + 128) with zp_a' | ||
We get our new quantized uint8 tensor - scale * (QA' - zp_a') | ||
Similarly we can convert from int8 to uint8. | ||
Parameters | ||
---------- | ||
attrs : tvm.attrs.Attrs | ||
Attributes of current convolution | ||
inputs : list of tvm.relay.Expr | ||
The args of the Relay expr to be legalized | ||
types : list of types | ||
List of input and output types | ||
Returns | ||
------- | ||
result : tvm.relay.Expr | ||
The legalized expr | ||
""" | ||
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def _shift(data, out_dtype): | ||
"""Shifts (add/subtracts) the qnn tensor with +/-128)""" | ||
if out_dtype == 'uint8': | ||
shift = 128 | ||
elif out_dtype == 'int8': | ||
shift = -128 | ||
else: | ||
raise ValueError("Unsupport out dtype.") | ||
data_modified = relay.cast(data, 'int32') | ||
data_modified = relay.add(data_modified, relay.const(shift, 'int32')) | ||
data_modified = relay.cast(data_modified, out_dtype) | ||
return data_modified | ||
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def _is_int8_hw_support(target): | ||
""" | ||
Checks to ensure that we can use Intel DLBoost instructions - Check if the target is skylake | ||
and above. | ||
""" | ||
supported_arches = {'-mcpu=skylake-avx512',} | ||
return supported_arches.intersection(set(target.options)) | ||
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# Collect the dtypes. | ||
data_dtype = types[0].dtype | ||
kernel_dtype = types[1].dtype | ||
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# Collect the input exprs. | ||
data, kernel = inputs | ||
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# The VNNI transformations are applicable only Skylake and above.g | ||
target = tvm.target.current_target(allow_none=False) | ||
if not _is_int8_hw_support(target): | ||
return None | ||
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# VNNI supports u8 x i8 fast conv/MM. Don't do anything if it is already satisfied. | ||
if data_dtype == 'uint8' and kernel_dtype == 'int8': | ||
return None | ||
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# Shift input if necessary. | ||
input_zp = attrs['input_zero_point'] | ||
if data_dtype == 'int8': | ||
# Compute (QA + 128) and (zp_a + 128) | ||
data = _shift(data, 'uint8') | ||
input_zp = input_zp + 128 | ||
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# Shift kernel if necessary. | ||
kernel_zp = attrs['kernel_zero_point'] | ||
if kernel_dtype == 'uint8': | ||
# Compute (QA - 128) and (zp_a - 128) | ||
kernel = _shift(kernel, 'int8') | ||
kernel_zp = kernel_zp - 128 | ||
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# Call qnn.conv2d with modified inputs and zero points. | ||
new_attrs = {k : attrs[k] for k in attrs.keys()} | ||
new_attrs['input_zero_point'] = input_zp | ||
new_attrs['kernel_zero_point'] = kernel_zp | ||
return relay.qnn.op.conv2d(data, kernel, **new_attrs) |
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# Licensed to the Apache Software Foundation (ASF) under one | ||
# or more contributor license agreements. See the NOTICE file | ||
# distributed with this work for additional information | ||
# regarding copyright ownership. The ASF licenses this file | ||
# to you under the Apache License, Version 2.0 (the | ||
# "License"); you may not use this file except in compliance | ||
# with the License. You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, | ||
# software distributed under the License is distributed on an | ||
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
# KIND, either express or implied. See the License for the | ||
# specific language governing permissions and limitations | ||
# under the License. | ||
"""The attributes node used for QNN operators""" | ||
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from ....attrs import Attrs | ||
from ...base import register_relay_attr_node | ||
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@register_relay_attr_node | ||
class QnnConv2DAttrs(Attrs): | ||
"""Attributes for qnn.conv2d""" |
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