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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/08/11 10:09:07 UTC
[GitHub] [tvm] lhutton1 commented on a diff in pull request #12353: [CMSIS-NN] Pad fusion with QNN Conv2D
lhutton1 commented on code in PR #12353:
URL: https://github.com/apache/tvm/pull/12353#discussion_r943274640
##########
src/relay/backend/contrib/cmsisnn/fuse_pads.cc:
##########
@@ -0,0 +1,219 @@
+
+/*
+ * 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.
+ */
+/*!
+ * \file fuse_pads.cc
Review Comment:
Nit: src/relay/backend/contrib/cmsisnn/fuse_pads.cc
##########
tests/python/contrib/test_cmsisnn/test_fuse_pads.py:
##########
@@ -0,0 +1,279 @@
+# 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.
+
+"""CMSIS-NN integration tests: fuse_pads pass"""
+import numpy as np
+import pytest
+import tvm
+import tvm.testing
+from tvm import relay
+
+tvm._ffi._init_api("relay.ext.cmsisnn.transform", __name__)
+
+
+class CheckForPadsWithinCompositeFunc(tvm.relay.ExprVisitor):
+ """Provides method to test number of pads present inside the function being visited."""
+
+ def __init__(self):
+ super().__init__()
+ self.num_pads_ = 0
+
+ def visit_call(self, call):
+ super().visit_call(call)
+ if (
+ isinstance(call, tvm.relay.Call)
+ and isinstance(call.op, tvm.ir.op.Op)
+ and call.op.name == "nn.pad"
+ ):
+ self.num_pads_ += 1
+
+ def check_num_pads(self):
+ assert self.num_pads_ == 0, "CMSIS-NN composite function should not have pads"
+
+
+def set_external_func_attr(func, compiler, ext_symbol):
+ func = func.with_attr("Primitive", tvm.tir.IntImm("int32", 1))
+ func = func.with_attr("Compiler", compiler)
+ func = func.with_attr("global_symbol", ext_symbol)
+ return func
+
+
+def set_composite_func_attr(func, name):
+ func = func.with_attr("Composite", name)
+ return func
+
+
+@pytest.mark.parametrize(
+ "ifm_shape, pad_width, conv2d_padding, ofm_shape",
+ [
+ [(1, 25, 25, 12), ((0, 0), (0, 2), (1, 2), (0, 0)), (1, 1, 1, 1), (1, 26, 28, 2)],
+ [(1, 64, 100, 4), ((0, 0), (1, 3), (1, 1), (0, 0)), (0, 0, 0, 0), (1, 64, 100, 2)],
+ [(1, 55, 55, 3), ((0, 0), (2, 1), (3, 5), (0, 0)), (0, 0, 1, 1), (1, 57, 59, 2)],
+ ],
+)
+def test_invalid_padding_for_fusion(ifm_shape, pad_width, conv2d_padding, ofm_shape):
+ """Negative tests for pads preceding Conv2D that cannot be fused."""
+ dtype = "int8"
+ kernel_size = (3, 3)
+ ofm_channels = 2
+ local_input = relay.var("local_input", shape=ifm_shape, dtype=dtype)
+ pad = relay.nn.pad(
+ local_input,
+ pad_width=pad_width, # ((), (top, bottom), (left, right), ())
+ pad_value=10,
+ pad_mode="constant",
+ )
+ rng = np.random.default_rng(12321)
+ local_weight = tvm.nd.array(
+ rng.integers(
+ np.iinfo(dtype).min,
+ high=np.iinfo(dtype).max,
+ size=(ofm_channels, kernel_size[0], kernel_size[1], ifm_shape[3]),
+ dtype=dtype,
+ )
+ )
+ local_weight = relay.const(local_weight, dtype)
+ conv2d = relay.qnn.op.conv2d(
+ pad,
+ local_weight,
+ relay.const(1, "int32"),
+ relay.const(1, "int32"),
+ relay.const(1, "float32"),
+ relay.const(1, "float32"),
+ data_layout="NHWC",
+ kernel_layout="OHWI",
+ channels=ofm_channels,
+ kernel_size=(3, 3),
+ padding=conv2d_padding,
+ out_dtype="int32",
+ )
+ requantize = relay.qnn.op.requantize(
+ conv2d,
+ relay.const(1, "float32"),
+ relay.const(1, "int32"),
+ relay.const(1, "float32"),
+ relay.const(1, "int32"),
+ axis=0,
+ out_dtype=dtype,
+ )
+ local_func = relay.Function(relay.analysis.free_vars(requantize), requantize)
+ local_func = set_composite_func_attr(local_func, "cmsis-nn.qnn_conv2d")
+
+ mod = tvm.IRModule()
+ ext_input = relay.var("ext_input", shape=ifm_shape, dtype=dtype)
+ call_local_func = relay.Call(local_func, [ext_input])
+ extern_func = relay.Function(relay.analysis.free_vars(call_local_func), call_local_func)
+ extern_var = relay.GlobalVar("external_function")
+ extern_func = set_external_func_attr(extern_func, "cmsis-nn", extern_var.name_hint)
+ mod[extern_var] = extern_func
+
+ main_input = relay.var("main_input", shape=ifm_shape, dtype=dtype)
+ call_extern_func = relay.Call(extern_var, [main_input])
+ main_func = relay.Function([main_input], call_extern_func, relay.TensorType(ofm_shape, dtype))
+ main_var = relay.GlobalVar("main")
+ mod[main_var] = main_func
+
+ mod = relay.transform.InferType()(mod)
+
+ error_regex = r"Difference on each side of a dimension should be either 0 or 1"
+
+ with pytest.raises(tvm.TVMError, match=error_regex):
+ mod = CMSISNNFusePads()(mod)
+
+
+@pytest.mark.parametrize(
+ "ifm_shape, pad_width, conv2d_padding, ofm_shape",
+ [
+ [(1, 25, 25, 12), ((0, 0), (0, 1), (1, 2), (0, 0)), (1, 1, 1, 1), (1, 26, 28, 2)],
+ [(1, 64, 100, 4), ((0, 0), (1, 1), (1, 1), (0, 0)), (0, 0, 0, 0), (1, 64, 100, 2)],
+ [(1, 55, 55, 3), ((0, 0), (2, 1), (3, 2), (0, 0)), (0, 0, 1, 1), (1, 57, 59, 2)],
+ ],
+)
+def test_pad_conv2d_fusion(ifm_shape, pad_width, conv2d_padding, ofm_shape):
+ """Tests the pads and conv2d fusion."""
+ dtype = "int8"
+ kernel_size = (3, 3)
+ ofm_channels = 2
+ local_input = relay.var("local_input", shape=ifm_shape, dtype=dtype)
+ pad = relay.nn.pad(
+ local_input,
+ pad_width=pad_width, # ((), (top, bottom), (left, right), ())
+ pad_value=10,
+ pad_mode="constant",
+ )
+ rng = np.random.default_rng(12321)
+ local_weight = tvm.nd.array(
+ rng.integers(
+ np.iinfo(dtype).min,
+ high=np.iinfo(dtype).max,
+ size=(ofm_channels, kernel_size[0], kernel_size[1], ifm_shape[3]),
+ dtype=dtype,
+ )
+ )
+ local_weight = relay.const(local_weight, dtype)
+ conv2d = relay.qnn.op.conv2d(
+ pad,
+ local_weight,
+ relay.const(1, "int32"),
+ relay.const(1, "int32"),
+ relay.const(1, "float32"),
+ relay.const(1, "float32"),
+ data_layout="NHWC",
+ kernel_layout="OHWI",
+ channels=ofm_channels,
+ kernel_size=(3, 3),
+ padding=conv2d_padding,
+ out_dtype="int32",
+ )
+ requantize = relay.qnn.op.requantize(
+ conv2d,
+ relay.const(1, "float32"),
+ relay.const(1, "int32"),
+ relay.const(1, "float32"),
+ relay.const(1, "int32"),
+ axis=0,
+ out_dtype=dtype,
+ )
+ local_func = relay.Function(relay.analysis.free_vars(requantize), requantize)
+ local_func = set_composite_func_attr(local_func, "cmsis-nn.qnn_conv2d")
+
+ mod = tvm.IRModule()
+ ext_input = relay.var("ext_input", shape=ifm_shape, dtype=dtype)
+ call_local_func = relay.Call(local_func, [ext_input])
+ extern_func = relay.Function(relay.analysis.free_vars(call_local_func), call_local_func)
+ extern_var = relay.GlobalVar("external_function")
+ extern_func = set_external_func_attr(extern_func, "cmsis-nn", extern_var.name_hint)
+ mod[extern_var] = extern_func
+
+ main_input = relay.var("main_input", shape=ifm_shape, dtype=dtype)
+ call_extern_func = relay.Call(extern_var, [main_input])
+ main_func = relay.Function([main_input], call_extern_func, relay.TensorType(ofm_shape, dtype))
+ main_var = relay.GlobalVar("main")
+ mod[main_var] = main_func
+
+ mod = relay.transform.InferType()(mod)
+
+ mod = CMSISNNFusePads()(mod)
+ pad_verifier = CheckForPadsWithinCompositeFunc()
+ pad_verifier.visit_function(mod[extern_var])
+ pad_verifier.check_num_pads()
+
+
+def test_without_preceding_pad():
+ """Tests the pass FusePads when padding is not present before qnn.conv2d."""
+ dtype = "int8"
+ ifm_shape = (1, 56, 56, 64)
+ ofm_shape = (1, 56, 56, 64)
+ local_input = relay.var("local_input", shape=ifm_shape, dtype=dtype)
+ rng = np.random.default_rng(12321)
+ local_weight = tvm.nd.array(
+ rng.integers(
+ np.iinfo(dtype).min,
+ high=np.iinfo(dtype).max,
+ size=(64, 3, 3, 64),
+ dtype=dtype,
+ )
+ )
+ local_weight = relay.const(local_weight, dtype)
+ conv2d = relay.qnn.op.conv2d(
+ local_input,
+ local_weight,
+ relay.const(1, "int32"),
+ relay.const(1, "int32"),
+ relay.const(1, "float32"),
+ relay.const(1, "float32"),
+ data_layout="NHWC",
+ kernel_layout="OHWI",
+ channels=64,
+ kernel_size=(3, 3),
+ padding=(1, 1, 1, 1),
+ out_dtype="int32",
+ )
+ requantize = relay.qnn.op.requantize(
+ conv2d,
+ relay.const(1, "float32"),
+ relay.const(1, "int32"),
+ relay.const(1, "float32"),
+ relay.const(1, "int32"),
+ axis=0,
+ out_dtype=dtype,
+ )
+ relu = relay.nn.relu(requantize)
+ local_func = relay.Function(relay.analysis.free_vars(relu), relu)
+ local_func = set_composite_func_attr(local_func, "cmsis-nn.qnn_conv2d")
+
+ mod = tvm.IRModule()
+ ext_input = relay.var("ext_input", shape=ifm_shape, dtype=dtype)
+ call_local_func = relay.Call(local_func, [ext_input])
+ extern_func = relay.Function(relay.analysis.free_vars(call_local_func), call_local_func)
+ extern_var = relay.GlobalVar("external_function")
+ extern_func = set_external_func_attr(extern_func, "cmsis-nn", extern_var.name_hint)
+ mod[extern_var] = extern_func
+
+ main_input = relay.var("main_input", shape=ifm_shape, dtype=dtype)
+ call_extern_func = relay.Call(extern_var, [main_input])
+ main_func = relay.Function(relay.analysis.free_vars(call_extern_func), call_extern_func)
+ main_func = relay.Function([main_input], call_extern_func, relay.TensorType(ofm_shape, dtype))
+ main_var = relay.GlobalVar("main")
+ mod[main_var] = main_func
+
+ mod = relay.transform.InferType()(mod)
+
+ mod = CMSISNNFusePads()(mod)
+ pad_verifier = CheckForPadsWithinCompositeFunc()
+ pad_verifier.visit_function(mod[extern_var])
+ pad_verifier.check_num_pads()
Review Comment:
Could we add a test to check that padding for non-cmsisnn functions is not altered?
##########
src/relay/backend/contrib/cmsisnn/fuse_pads.cc:
##########
@@ -0,0 +1,219 @@
+
+/*
+ * 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.
+ */
+/*!
+ * \file fuse_pads.cc
+ * \brief Fuses pads that precede qnn.conv2d ops inside CMSIS-NN composite functions.
+ */
+
+#include <tvm/relay/attrs/nn.h>
+#include <tvm/relay/attrs/transform.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/transform.h>
+#include <tvm/runtime/ndarray.h>
+
+#include "../../../op/make_op.h"
+#include "../../../qnn/utils.h"
+#include "../../../transforms/pattern_utils.h"
+#include "convolutions.h"
+
+namespace tvm {
+namespace relay {
+namespace contrib {
+namespace cmsisnn {
+
+/*!
+ * \brief This Mutator will find all partitioned functions meant for CMSIS-NN Conv2D.
+ * Then, it will fuse preceding pads with qnn.conv2d.
+ */
+class FusePadsMutator : public MixedModeMutator {
+ public:
+ explicit FusePadsMutator(const IRModule& mod) : mod_(mod) {}
+
+ private:
+ /*! * \brief In order to eliminate preceding nn.pad op, pad_width of nn.pad is passed onto
+ * convolution layer to update Conv2DAttrs's padding attribute. */
+ void UpdateConv2DPadding(const CallNode* conv2d_call, const Array<Array<Integer>>& pad_width,
+ const Conv2DAttrs* conv2d_attrs, Attrs* new_attrs) {
+ auto attrs = make_object<Conv2DAttrs>();
+ attrs->strides = std::move(conv2d_attrs->strides);
+ attrs->dilation = std::move(conv2d_attrs->dilation);
+ attrs->groups = conv2d_attrs->groups;
+ attrs->channels = std::move(conv2d_attrs->channels);
+ attrs->kernel_size = std::move(conv2d_attrs->kernel_size);
+ attrs->data_layout = std::move(conv2d_attrs->data_layout);
+ attrs->kernel_layout = std::move(conv2d_attrs->kernel_layout);
+ attrs->out_layout = std::move(conv2d_attrs->out_layout);
+ attrs->out_dtype = std::move(conv2d_attrs->out_dtype);
Review Comment:
Is it possible to copy conv2d_attrs instead?
##########
src/relay/backend/contrib/cmsisnn/fuse_pads.cc:
##########
@@ -0,0 +1,219 @@
+
+/*
+ * 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.
+ */
+/*!
+ * \file fuse_pads.cc
+ * \brief Fuses pads that precede qnn.conv2d ops inside CMSIS-NN composite functions.
+ */
+
+#include <tvm/relay/attrs/nn.h>
+#include <tvm/relay/attrs/transform.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/transform.h>
+#include <tvm/runtime/ndarray.h>
+
+#include "../../../op/make_op.h"
+#include "../../../qnn/utils.h"
+#include "../../../transforms/pattern_utils.h"
+#include "convolutions.h"
+
+namespace tvm {
+namespace relay {
+namespace contrib {
+namespace cmsisnn {
+
+/*!
+ * \brief This Mutator will find all partitioned functions meant for CMSIS-NN Conv2D.
+ * Then, it will fuse preceding pads with qnn.conv2d.
+ */
+class FusePadsMutator : public MixedModeMutator {
+ public:
+ explicit FusePadsMutator(const IRModule& mod) : mod_(mod) {}
+
+ private:
+ /*! * \brief In order to eliminate preceding nn.pad op, pad_width of nn.pad is passed onto
+ * convolution layer to update Conv2DAttrs's padding attribute. */
+ void UpdateConv2DPadding(const CallNode* conv2d_call, const Array<Array<Integer>>& pad_width,
+ const Conv2DAttrs* conv2d_attrs, Attrs* new_attrs) {
+ auto attrs = make_object<Conv2DAttrs>();
+ attrs->strides = std::move(conv2d_attrs->strides);
+ attrs->dilation = std::move(conv2d_attrs->dilation);
+ attrs->groups = conv2d_attrs->groups;
+ attrs->channels = std::move(conv2d_attrs->channels);
+ attrs->kernel_size = std::move(conv2d_attrs->kernel_size);
+ attrs->data_layout = std::move(conv2d_attrs->data_layout);
+ attrs->kernel_layout = std::move(conv2d_attrs->kernel_layout);
+ attrs->out_layout = std::move(conv2d_attrs->out_layout);
+ attrs->out_dtype = std::move(conv2d_attrs->out_dtype);
+
+ // pad_width: ((), (top, bottom), (left, right), ()) for NHWC layout
+ // conv2d_attrs->padding: (top, left, bottom, right)
+ std::string data_layout = conv2d_attrs->data_layout.c_str();
+ int pos_h = data_layout.find("H");
+ int pos_w = data_layout.find("W");
+
+ int pad_top =
+ qnn::get_const_int(conv2d_attrs->padding[0]) + qnn::get_const_int(pad_width[pos_h][0]);
+ int pad_left =
+ qnn::get_const_int(conv2d_attrs->padding[1]) + qnn::get_const_int(pad_width[pos_w][0]);
+ int pad_bottom =
+ qnn::get_const_int(conv2d_attrs->padding[2]) + qnn::get_const_int(pad_width[pos_h][1]);
+ int pad_right =
+ qnn::get_const_int(conv2d_attrs->padding[3]) + qnn::get_const_int(pad_width[pos_w][1]);
+
+ int pad_diff_w = pad_right - pad_left;
+ int pad_diff_h = pad_bottom - pad_top;
+ bool can_pad_be_fused =
+ ((pad_diff_w == 0 || pad_diff_w == 1) && (pad_diff_h == 0 || pad_diff_h == 1));
+ std::string error = "Difference on each side of a dimension should be either 0 or 1. ";
+ error += "Effective padding in this case: (pad_top, pad_left, pad_bottom, pad_right)=(";
+ error += std::to_string(pad_top);
+ error += ", ";
+ error += std::to_string(pad_left);
+ error += ", ";
+ error += std::to_string(pad_bottom);
+ error += ", ";
+ error += std::to_string(pad_right);
+ error += ")";
+ ICHECK(can_pad_be_fused) << error;
+
+ attrs->padding = {pad_top, pad_left, pad_bottom, pad_right};
+ *new_attrs = tvm::Attrs{attrs};
+ }
+
+ /*!
+ * \brief Identifies the sequence for qnn.conv2D and fuses the preceding nn.pad present within the
+ * CMSIS-NN partitioned function. */
+ Expr FusePadConv2d(const Expr& expr) {
+ const CallNode* clip_call = nullptr;
+ const CallNode* requantize_call = nullptr;
+ const CallNode* bias_add_call = nullptr;
Review Comment:
Curious, why do we need to maintain references to these ops and reconstruct them at the end of this function? Will a call to `VisitExpr` after the new conv2d has been created do this for us instead?
##########
tests/python/contrib/test_cmsisnn/test_conv2d.py:
##########
@@ -729,7 +830,7 @@ def test_invalid_parameters(
in_dtype,
kernel_dtype,
in_dtype,
- False,
+ is_depthwise=False,
)
model, params = make_model(
shape=ifm_shape,
Review Comment:
Would be good to check a couple of other things here:
- The pass runs as part of the pipeline as these tests could pass even when the pass is not run
- Attempting to fuse a pad operation that is unsupported falls back to offloading just conv2d (think we might have missed this case in microNPU)
##########
src/relay/backend/contrib/cmsisnn/fuse_pads.cc:
##########
@@ -0,0 +1,219 @@
+
Review Comment:
Nit: additional line here?
##########
src/relay/backend/contrib/cmsisnn/fuse_pads.cc:
##########
@@ -0,0 +1,219 @@
+
+/*
+ * 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.
+ */
+/*!
+ * \file fuse_pads.cc
+ * \brief Fuses pads that precede qnn.conv2d ops inside CMSIS-NN composite functions.
+ */
+
+#include <tvm/relay/attrs/nn.h>
+#include <tvm/relay/attrs/transform.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/transform.h>
+#include <tvm/runtime/ndarray.h>
+
+#include "../../../op/make_op.h"
+#include "../../../qnn/utils.h"
+#include "../../../transforms/pattern_utils.h"
+#include "convolutions.h"
+
+namespace tvm {
+namespace relay {
+namespace contrib {
+namespace cmsisnn {
+
+/*!
+ * \brief This Mutator will find all partitioned functions meant for CMSIS-NN Conv2D.
+ * Then, it will fuse preceding pads with qnn.conv2d.
+ */
+class FusePadsMutator : public MixedModeMutator {
+ public:
+ explicit FusePadsMutator(const IRModule& mod) : mod_(mod) {}
+
+ private:
+ /*! * \brief In order to eliminate preceding nn.pad op, pad_width of nn.pad is passed onto
+ * convolution layer to update Conv2DAttrs's padding attribute. */
+ void UpdateConv2DPadding(const CallNode* conv2d_call, const Array<Array<Integer>>& pad_width,
+ const Conv2DAttrs* conv2d_attrs, Attrs* new_attrs) {
+ auto attrs = make_object<Conv2DAttrs>();
+ attrs->strides = std::move(conv2d_attrs->strides);
+ attrs->dilation = std::move(conv2d_attrs->dilation);
+ attrs->groups = conv2d_attrs->groups;
+ attrs->channels = std::move(conv2d_attrs->channels);
+ attrs->kernel_size = std::move(conv2d_attrs->kernel_size);
+ attrs->data_layout = std::move(conv2d_attrs->data_layout);
+ attrs->kernel_layout = std::move(conv2d_attrs->kernel_layout);
+ attrs->out_layout = std::move(conv2d_attrs->out_layout);
+ attrs->out_dtype = std::move(conv2d_attrs->out_dtype);
+
+ // pad_width: ((), (top, bottom), (left, right), ()) for NHWC layout
+ // conv2d_attrs->padding: (top, left, bottom, right)
+ std::string data_layout = conv2d_attrs->data_layout.c_str();
+ int pos_h = data_layout.find("H");
+ int pos_w = data_layout.find("W");
+
+ int pad_top =
+ qnn::get_const_int(conv2d_attrs->padding[0]) + qnn::get_const_int(pad_width[pos_h][0]);
+ int pad_left =
+ qnn::get_const_int(conv2d_attrs->padding[1]) + qnn::get_const_int(pad_width[pos_w][0]);
+ int pad_bottom =
+ qnn::get_const_int(conv2d_attrs->padding[2]) + qnn::get_const_int(pad_width[pos_h][1]);
+ int pad_right =
+ qnn::get_const_int(conv2d_attrs->padding[3]) + qnn::get_const_int(pad_width[pos_w][1]);
+
+ int pad_diff_w = pad_right - pad_left;
+ int pad_diff_h = pad_bottom - pad_top;
Review Comment:
Is it possible to separate this logic and register it globally, so we can use it in python in the check function as well to avoid duplication?
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