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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/01/04 19:10:57 UTC
[GitHub] [tvm] d-smirnov opened a new pull request #7206: [BYOC][ACL] Depthwise convolution support
d-smirnov opened a new pull request #7206:
URL: https://github.com/apache/tvm/pull/7206
Added support for depthwise convolution. ACL only supports depth-wise convolution when kernel size is 3x3 and 5x5 and strides are (1, 1) or (2, 2), if this is not the case then fallback to TVM.
Also rework tests to remove non-deterministic trials.
- ACL is Compute Library for the Arm Architecture
- All credits to Luke Hutton @lhutton1
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[GitHub] [tvm] d-smirnov commented on a change in pull request #7206: [BYOC][ACL] Depthwise convolution support
Posted by GitBox <gi...@apache.org>.
d-smirnov commented on a change in pull request #7206:
URL: https://github.com/apache/tvm/pull/7206#discussion_r553995726
##########
File path: python/tvm/relay/op/contrib/arm_compute_lib.py
##########
@@ -19,12 +19,15 @@
import numpy as np
import tvm
+import tvm._ffi
Review comment:
Done
##########
File path: python/tvm/relay/op/contrib/arm_compute_lib.py
##########
@@ -71,6 +74,61 @@ def partition_for_arm_compute_lib(mod, params=None):
return seq(mod)
+@tvm._ffi.register_func("relay.ext.arm_compute_lib.optimize")
Review comment:
Done
##########
File path: python/tvm/relay/op/contrib/arm_compute_lib.py
##########
@@ -71,6 +74,61 @@ def partition_for_arm_compute_lib(mod, params=None):
return seq(mod)
+@tvm._ffi.register_func("relay.ext.arm_compute_lib.optimize")
+def preprocess_module(mod):
+ """
+ Pre-process a module containing functions ready for ACL codegen. For now we enforce OHWI
+ kernel layout and fold the transforms away.
+
+ ` Parameters
Review comment:
Done
##########
File path: src/relay/backend/contrib/arm_compute_lib/codegen.cc
##########
@@ -126,7 +127,7 @@ class ACLJSONSerializer : public backend::contrib::JSONSerializer {
nodes.activation = current_call;
current_call = current_call->args[0].as<CallNode>();
}
- if (backend::IsOp(current_call, "nn.bias_add")) {
+ if (backend::IsOp(current_call, "add")) {
Review comment:
It will be "lowered" (not sure I am using correct terminology) to add on time when the runtime is invoked
##########
File path: src/runtime/contrib/arm_compute_lib/acl_runtime.cc
##########
@@ -269,6 +268,64 @@ class ACLRuntime : public JSONRuntimeBase {
layer->function = function;
}
+ /*!
+ * \brief Create a 2D depthwise convolution layer.
+ *
+ * \param layer The ACL layer to build. Containing inputs, outputs and the ACL function.
+ * \param node The JSON representation of the operator.
+ * \param mm The ACL conv2d layer can request auxiliary memory from TVM.
+ */
+ void CreateDepthwiseConvolution2DLayer(
+ CachedLayer* layer, const JSONGraphNode& node,
+ const std::shared_ptr<arm_compute::MemoryManagerOnDemand>& mm) {
+ std::vector<std::string> padding = node.GetAttr<std::vector<std::string>>("padding");
+ std::vector<std::string> strides = node.GetAttr<std::vector<std::string>>("strides");
+ std::vector<std::string> dilation = node.GetAttr<std::vector<std::string>>("dilation");
+ arm_compute::PadStrideInfo pad_stride_info = MakeACLPadStride(padding, strides);
+
+ arm_compute::ActivationLayerInfo act_info;
+ if (node.HasAttr("activation_type")) {
+ std::string activation_type = node.GetAttr<std::vector<std::string>>("activation_type")[0];
+ act_info = MakeACLActivationInfo(activation_type);
+ }
+
+ arm_compute::Size2D dilation_2d(std::stoi(dilation[0]), std::stoi(dilation[1]));
+
+ // Collect inputs and outputs, handling both nn.conv2d and qnn.conv2d cases.
+ std::vector<JSONGraphNodeEntry> inputs = node.GetInputs();
+ size_t num_inputs = inputs.size();
+ bool has_bias;
+ if (node.GetOpName() == "qnn.depthwise_conv2d") {
+ CHECK(num_inputs >= 8U && num_inputs <= 9U)
+ << "Quantized convolution requires 9 inputs with a bias, 8 inputs without.";
+ has_bias = num_inputs == 9;
+ layer->inputs.push_back(MakeACLTensorFromJSONEntry(inputs[0], &inputs[4], &inputs[2]));
+ layer->inputs.push_back(MakeACLTensorFromJSONEntry(inputs[1], &inputs[5], &inputs[3]));
+ if (has_bias) {
+ layer->inputs.push_back(MakeACLTensorFromJSONEntry(inputs[6]));
+ }
+ layer->outputs.push_back(
+ MakeACLTensorFromJSONNode(node, &inputs[6 + has_bias], &inputs[7 + has_bias]));
+ } else {
+ CHECK(num_inputs >= 2U && num_inputs <= 3U)
Review comment:
Yes it should. Thank you.
##########
File path: src/runtime/contrib/arm_compute_lib/acl_runtime.cc
##########
@@ -269,6 +268,64 @@ class ACLRuntime : public JSONRuntimeBase {
layer->function = function;
}
+ /*!
+ * \brief Create a 2D depthwise convolution layer.
+ *
+ * \param layer The ACL layer to build. Containing inputs, outputs and the ACL function.
+ * \param node The JSON representation of the operator.
+ * \param mm The ACL conv2d layer can request auxiliary memory from TVM.
+ */
+ void CreateDepthwiseConvolution2DLayer(
+ CachedLayer* layer, const JSONGraphNode& node,
+ const std::shared_ptr<arm_compute::MemoryManagerOnDemand>& mm) {
+ std::vector<std::string> padding = node.GetAttr<std::vector<std::string>>("padding");
+ std::vector<std::string> strides = node.GetAttr<std::vector<std::string>>("strides");
+ std::vector<std::string> dilation = node.GetAttr<std::vector<std::string>>("dilation");
+ arm_compute::PadStrideInfo pad_stride_info = MakeACLPadStride(padding, strides);
+
+ arm_compute::ActivationLayerInfo act_info;
+ if (node.HasAttr("activation_type")) {
+ std::string activation_type = node.GetAttr<std::vector<std::string>>("activation_type")[0];
+ act_info = MakeACLActivationInfo(activation_type);
+ }
+
+ arm_compute::Size2D dilation_2d(std::stoi(dilation[0]), std::stoi(dilation[1]));
+
+ // Collect inputs and outputs, handling both nn.conv2d and qnn.conv2d cases.
+ std::vector<JSONGraphNodeEntry> inputs = node.GetInputs();
+ size_t num_inputs = inputs.size();
+ bool has_bias;
+ if (node.GetOpName() == "qnn.depthwise_conv2d") {
+ CHECK(num_inputs >= 8U && num_inputs <= 9U)
Review comment:
Yes it should. Thank you.
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[GitHub] [tvm] lhutton1 commented on a change in pull request #7206: [BYOC][ACL] Depthwise convolution support
Posted by GitBox <gi...@apache.org>.
lhutton1 commented on a change in pull request #7206:
URL: https://github.com/apache/tvm/pull/7206#discussion_r554048506
##########
File path: src/runtime/contrib/arm_compute_lib/acl_runtime.cc
##########
@@ -269,6 +268,64 @@ class ACLRuntime : public JSONRuntimeBase {
layer->function = function;
}
+ /*!
+ * \brief Create a 2D depthwise convolution layer.
+ *
+ * \param layer The ACL layer to build. Containing inputs, outputs and the ACL function.
+ * \param node The JSON representation of the operator.
+ * \param mm The ACL conv2d layer can request auxiliary memory from TVM.
+ */
+ void CreateDepthwiseConvolution2DLayer(
+ CachedLayer* layer, const JSONGraphNode& node,
+ const std::shared_ptr<arm_compute::MemoryManagerOnDemand>& mm) {
+ std::vector<std::string> padding = node.GetAttr<std::vector<std::string>>("padding");
+ std::vector<std::string> strides = node.GetAttr<std::vector<std::string>>("strides");
+ std::vector<std::string> dilation = node.GetAttr<std::vector<std::string>>("dilation");
+ arm_compute::PadStrideInfo pad_stride_info = MakeACLPadStride(padding, strides);
+
+ arm_compute::ActivationLayerInfo act_info;
+ if (node.HasAttr("activation_type")) {
+ std::string activation_type = node.GetAttr<std::vector<std::string>>("activation_type")[0];
+ act_info = MakeACLActivationInfo(activation_type);
+ }
+
+ arm_compute::Size2D dilation_2d(std::stoi(dilation[0]), std::stoi(dilation[1]));
+
+ // Collect inputs and outputs, handling both nn.conv2d and qnn.conv2d cases.
+ std::vector<JSONGraphNodeEntry> inputs = node.GetInputs();
+ size_t num_inputs = inputs.size();
+ bool has_bias;
+ if (node.GetOpName() == "qnn.depthwise_conv2d") {
+ CHECK(num_inputs >= 8U && num_inputs <= 9U)
Review comment:
I'm not too familiar with the new conventions, but should this be `ICHECK`?
##########
File path: src/runtime/contrib/arm_compute_lib/acl_runtime.cc
##########
@@ -269,6 +268,64 @@ class ACLRuntime : public JSONRuntimeBase {
layer->function = function;
}
+ /*!
+ * \brief Create a 2D depthwise convolution layer.
+ *
+ * \param layer The ACL layer to build. Containing inputs, outputs and the ACL function.
+ * \param node The JSON representation of the operator.
+ * \param mm The ACL conv2d layer can request auxiliary memory from TVM.
+ */
+ void CreateDepthwiseConvolution2DLayer(
+ CachedLayer* layer, const JSONGraphNode& node,
+ const std::shared_ptr<arm_compute::MemoryManagerOnDemand>& mm) {
+ std::vector<std::string> padding = node.GetAttr<std::vector<std::string>>("padding");
+ std::vector<std::string> strides = node.GetAttr<std::vector<std::string>>("strides");
+ std::vector<std::string> dilation = node.GetAttr<std::vector<std::string>>("dilation");
+ arm_compute::PadStrideInfo pad_stride_info = MakeACLPadStride(padding, strides);
+
+ arm_compute::ActivationLayerInfo act_info;
+ if (node.HasAttr("activation_type")) {
+ std::string activation_type = node.GetAttr<std::vector<std::string>>("activation_type")[0];
+ act_info = MakeACLActivationInfo(activation_type);
+ }
+
+ arm_compute::Size2D dilation_2d(std::stoi(dilation[0]), std::stoi(dilation[1]));
+
+ // Collect inputs and outputs, handling both nn.conv2d and qnn.conv2d cases.
+ std::vector<JSONGraphNodeEntry> inputs = node.GetInputs();
+ size_t num_inputs = inputs.size();
+ bool has_bias;
+ if (node.GetOpName() == "qnn.depthwise_conv2d") {
+ CHECK(num_inputs >= 8U && num_inputs <= 9U)
+ << "Quantized convolution requires 9 inputs with a bias, 8 inputs without.";
+ has_bias = num_inputs == 9;
+ layer->inputs.push_back(MakeACLTensorFromJSONEntry(inputs[0], &inputs[4], &inputs[2]));
+ layer->inputs.push_back(MakeACLTensorFromJSONEntry(inputs[1], &inputs[5], &inputs[3]));
+ if (has_bias) {
+ layer->inputs.push_back(MakeACLTensorFromJSONEntry(inputs[6]));
+ }
+ layer->outputs.push_back(
+ MakeACLTensorFromJSONNode(node, &inputs[6 + has_bias], &inputs[7 + has_bias]));
+ } else {
+ CHECK(num_inputs >= 2U && num_inputs <= 3U)
Review comment:
Should be `ICHECK`?
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[GitHub] [tvm] mbaret merged pull request #7206: [BYOC][ACL] Depthwise convolution support
Posted by GitBox <gi...@apache.org>.
mbaret merged pull request #7206:
URL: https://github.com/apache/tvm/pull/7206
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[GitHub] [tvm] d-smirnov commented on a change in pull request #7206: [BYOC][ACL] Depthwise convolution support
Posted by GitBox <gi...@apache.org>.
d-smirnov commented on a change in pull request #7206:
URL: https://github.com/apache/tvm/pull/7206#discussion_r553995853
##########
File path: python/tvm/relay/op/contrib/arm_compute_lib.py
##########
@@ -71,6 +74,61 @@ def partition_for_arm_compute_lib(mod, params=None):
return seq(mod)
+@tvm._ffi.register_func("relay.ext.arm_compute_lib.optimize")
Review comment:
Done
----------------------------------------------------------------
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[GitHub] [tvm] lhutton1 commented on a change in pull request #7206: [BYOC][ACL] Depthwise convolution support
Posted by GitBox <gi...@apache.org>.
lhutton1 commented on a change in pull request #7206:
URL: https://github.com/apache/tvm/pull/7206#discussion_r551595603
##########
File path: python/tvm/relay/op/contrib/arm_compute_lib.py
##########
@@ -19,12 +19,15 @@
import numpy as np
import tvm
+import tvm._ffi
Review comment:
Better to use more specific import
```suggestion
from tvm._ffi import register_func
```
##########
File path: python/tvm/relay/op/contrib/arm_compute_lib.py
##########
@@ -71,6 +74,61 @@ def partition_for_arm_compute_lib(mod, params=None):
return seq(mod)
+@tvm._ffi.register_func("relay.ext.arm_compute_lib.optimize")
Review comment:
With above change
```suggestion
@register_func("relay.ext.arm_compute_lib.optimize")
```
##########
File path: python/tvm/relay/op/contrib/arm_compute_lib.py
##########
@@ -71,6 +74,61 @@ def partition_for_arm_compute_lib(mod, params=None):
return seq(mod)
+@tvm._ffi.register_func("relay.ext.arm_compute_lib.optimize")
+def preprocess_module(mod):
+ """
+ Pre-process a module containing functions ready for ACL codegen. For now we enforce OHWI
+ kernel layout and fold the transforms away.
+
+ ` Parameters
Review comment:
Remove `
##########
File path: src/relay/backend/contrib/arm_compute_lib/codegen.cc
##########
@@ -126,7 +127,7 @@ class ACLJSONSerializer : public backend::contrib::JSONSerializer {
nodes.activation = current_call;
current_call = current_call->args[0].as<CallNode>();
}
- if (backend::IsOp(current_call, "nn.bias_add")) {
+ if (backend::IsOp(current_call, "add")) {
Review comment:
I remember needing to change this but I didn't get to the bottom of why. Is there an explanation?
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[GitHub] [tvm] d-smirnov commented on a change in pull request #7206: [BYOC][ACL] Depthwise convolution support
Posted by GitBox <gi...@apache.org>.
d-smirnov commented on a change in pull request #7206:
URL: https://github.com/apache/tvm/pull/7206#discussion_r553995726
##########
File path: python/tvm/relay/op/contrib/arm_compute_lib.py
##########
@@ -19,12 +19,15 @@
import numpy as np
import tvm
+import tvm._ffi
Review comment:
Done
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[GitHub] [tvm] d-smirnov commented on a change in pull request #7206: [BYOC][ACL] Depthwise convolution support
Posted by GitBox <gi...@apache.org>.
d-smirnov commented on a change in pull request #7206:
URL: https://github.com/apache/tvm/pull/7206#discussion_r554187386
##########
File path: src/runtime/contrib/arm_compute_lib/acl_runtime.cc
##########
@@ -269,6 +268,64 @@ class ACLRuntime : public JSONRuntimeBase {
layer->function = function;
}
+ /*!
+ * \brief Create a 2D depthwise convolution layer.
+ *
+ * \param layer The ACL layer to build. Containing inputs, outputs and the ACL function.
+ * \param node The JSON representation of the operator.
+ * \param mm The ACL conv2d layer can request auxiliary memory from TVM.
+ */
+ void CreateDepthwiseConvolution2DLayer(
+ CachedLayer* layer, const JSONGraphNode& node,
+ const std::shared_ptr<arm_compute::MemoryManagerOnDemand>& mm) {
+ std::vector<std::string> padding = node.GetAttr<std::vector<std::string>>("padding");
+ std::vector<std::string> strides = node.GetAttr<std::vector<std::string>>("strides");
+ std::vector<std::string> dilation = node.GetAttr<std::vector<std::string>>("dilation");
+ arm_compute::PadStrideInfo pad_stride_info = MakeACLPadStride(padding, strides);
+
+ arm_compute::ActivationLayerInfo act_info;
+ if (node.HasAttr("activation_type")) {
+ std::string activation_type = node.GetAttr<std::vector<std::string>>("activation_type")[0];
+ act_info = MakeACLActivationInfo(activation_type);
+ }
+
+ arm_compute::Size2D dilation_2d(std::stoi(dilation[0]), std::stoi(dilation[1]));
+
+ // Collect inputs and outputs, handling both nn.conv2d and qnn.conv2d cases.
+ std::vector<JSONGraphNodeEntry> inputs = node.GetInputs();
+ size_t num_inputs = inputs.size();
+ bool has_bias;
+ if (node.GetOpName() == "qnn.depthwise_conv2d") {
+ CHECK(num_inputs >= 8U && num_inputs <= 9U)
+ << "Quantized convolution requires 9 inputs with a bias, 8 inputs without.";
+ has_bias = num_inputs == 9;
+ layer->inputs.push_back(MakeACLTensorFromJSONEntry(inputs[0], &inputs[4], &inputs[2]));
+ layer->inputs.push_back(MakeACLTensorFromJSONEntry(inputs[1], &inputs[5], &inputs[3]));
+ if (has_bias) {
+ layer->inputs.push_back(MakeACLTensorFromJSONEntry(inputs[6]));
+ }
+ layer->outputs.push_back(
+ MakeACLTensorFromJSONNode(node, &inputs[6 + has_bias], &inputs[7 + has_bias]));
+ } else {
+ CHECK(num_inputs >= 2U && num_inputs <= 3U)
Review comment:
Yes it should. Thank you.
##########
File path: src/runtime/contrib/arm_compute_lib/acl_runtime.cc
##########
@@ -269,6 +268,64 @@ class ACLRuntime : public JSONRuntimeBase {
layer->function = function;
}
+ /*!
+ * \brief Create a 2D depthwise convolution layer.
+ *
+ * \param layer The ACL layer to build. Containing inputs, outputs and the ACL function.
+ * \param node The JSON representation of the operator.
+ * \param mm The ACL conv2d layer can request auxiliary memory from TVM.
+ */
+ void CreateDepthwiseConvolution2DLayer(
+ CachedLayer* layer, const JSONGraphNode& node,
+ const std::shared_ptr<arm_compute::MemoryManagerOnDemand>& mm) {
+ std::vector<std::string> padding = node.GetAttr<std::vector<std::string>>("padding");
+ std::vector<std::string> strides = node.GetAttr<std::vector<std::string>>("strides");
+ std::vector<std::string> dilation = node.GetAttr<std::vector<std::string>>("dilation");
+ arm_compute::PadStrideInfo pad_stride_info = MakeACLPadStride(padding, strides);
+
+ arm_compute::ActivationLayerInfo act_info;
+ if (node.HasAttr("activation_type")) {
+ std::string activation_type = node.GetAttr<std::vector<std::string>>("activation_type")[0];
+ act_info = MakeACLActivationInfo(activation_type);
+ }
+
+ arm_compute::Size2D dilation_2d(std::stoi(dilation[0]), std::stoi(dilation[1]));
+
+ // Collect inputs and outputs, handling both nn.conv2d and qnn.conv2d cases.
+ std::vector<JSONGraphNodeEntry> inputs = node.GetInputs();
+ size_t num_inputs = inputs.size();
+ bool has_bias;
+ if (node.GetOpName() == "qnn.depthwise_conv2d") {
+ CHECK(num_inputs >= 8U && num_inputs <= 9U)
Review comment:
Yes it should. Thank you.
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[GitHub] [tvm] d-smirnov commented on a change in pull request #7206: [BYOC][ACL] Depthwise convolution support
Posted by GitBox <gi...@apache.org>.
d-smirnov commented on a change in pull request #7206:
URL: https://github.com/apache/tvm/pull/7206#discussion_r553996079
##########
File path: python/tvm/relay/op/contrib/arm_compute_lib.py
##########
@@ -71,6 +74,61 @@ def partition_for_arm_compute_lib(mod, params=None):
return seq(mod)
+@tvm._ffi.register_func("relay.ext.arm_compute_lib.optimize")
+def preprocess_module(mod):
+ """
+ Pre-process a module containing functions ready for ACL codegen. For now we enforce OHWI
+ kernel layout and fold the transforms away.
+
+ ` Parameters
Review comment:
Done
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[GitHub] [tvm] d-smirnov commented on a change in pull request #7206: [BYOC][ACL] Depthwise convolution support
Posted by GitBox <gi...@apache.org>.
d-smirnov commented on a change in pull request #7206:
URL: https://github.com/apache/tvm/pull/7206#discussion_r553997004
##########
File path: src/relay/backend/contrib/arm_compute_lib/codegen.cc
##########
@@ -126,7 +127,7 @@ class ACLJSONSerializer : public backend::contrib::JSONSerializer {
nodes.activation = current_call;
current_call = current_call->args[0].as<CallNode>();
}
- if (backend::IsOp(current_call, "nn.bias_add")) {
+ if (backend::IsOp(current_call, "add")) {
Review comment:
It will be "lowered" (not sure I am using correct terminology) to add on time when the runtime is invoked
----------------------------------------------------------------
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[GitHub] [tvm] lhutton1 commented on a change in pull request #7206: [BYOC][ACL] Depthwise convolution support
Posted by GitBox <gi...@apache.org>.
lhutton1 commented on a change in pull request #7206:
URL: https://github.com/apache/tvm/pull/7206#discussion_r554048506
##########
File path: src/runtime/contrib/arm_compute_lib/acl_runtime.cc
##########
@@ -269,6 +268,64 @@ class ACLRuntime : public JSONRuntimeBase {
layer->function = function;
}
+ /*!
+ * \brief Create a 2D depthwise convolution layer.
+ *
+ * \param layer The ACL layer to build. Containing inputs, outputs and the ACL function.
+ * \param node The JSON representation of the operator.
+ * \param mm The ACL conv2d layer can request auxiliary memory from TVM.
+ */
+ void CreateDepthwiseConvolution2DLayer(
+ CachedLayer* layer, const JSONGraphNode& node,
+ const std::shared_ptr<arm_compute::MemoryManagerOnDemand>& mm) {
+ std::vector<std::string> padding = node.GetAttr<std::vector<std::string>>("padding");
+ std::vector<std::string> strides = node.GetAttr<std::vector<std::string>>("strides");
+ std::vector<std::string> dilation = node.GetAttr<std::vector<std::string>>("dilation");
+ arm_compute::PadStrideInfo pad_stride_info = MakeACLPadStride(padding, strides);
+
+ arm_compute::ActivationLayerInfo act_info;
+ if (node.HasAttr("activation_type")) {
+ std::string activation_type = node.GetAttr<std::vector<std::string>>("activation_type")[0];
+ act_info = MakeACLActivationInfo(activation_type);
+ }
+
+ arm_compute::Size2D dilation_2d(std::stoi(dilation[0]), std::stoi(dilation[1]));
+
+ // Collect inputs and outputs, handling both nn.conv2d and qnn.conv2d cases.
+ std::vector<JSONGraphNodeEntry> inputs = node.GetInputs();
+ size_t num_inputs = inputs.size();
+ bool has_bias;
+ if (node.GetOpName() == "qnn.depthwise_conv2d") {
+ CHECK(num_inputs >= 8U && num_inputs <= 9U)
Review comment:
I'm not too familiar with the new conventions, but should this be `ICHECK`?
##########
File path: src/runtime/contrib/arm_compute_lib/acl_runtime.cc
##########
@@ -269,6 +268,64 @@ class ACLRuntime : public JSONRuntimeBase {
layer->function = function;
}
+ /*!
+ * \brief Create a 2D depthwise convolution layer.
+ *
+ * \param layer The ACL layer to build. Containing inputs, outputs and the ACL function.
+ * \param node The JSON representation of the operator.
+ * \param mm The ACL conv2d layer can request auxiliary memory from TVM.
+ */
+ void CreateDepthwiseConvolution2DLayer(
+ CachedLayer* layer, const JSONGraphNode& node,
+ const std::shared_ptr<arm_compute::MemoryManagerOnDemand>& mm) {
+ std::vector<std::string> padding = node.GetAttr<std::vector<std::string>>("padding");
+ std::vector<std::string> strides = node.GetAttr<std::vector<std::string>>("strides");
+ std::vector<std::string> dilation = node.GetAttr<std::vector<std::string>>("dilation");
+ arm_compute::PadStrideInfo pad_stride_info = MakeACLPadStride(padding, strides);
+
+ arm_compute::ActivationLayerInfo act_info;
+ if (node.HasAttr("activation_type")) {
+ std::string activation_type = node.GetAttr<std::vector<std::string>>("activation_type")[0];
+ act_info = MakeACLActivationInfo(activation_type);
+ }
+
+ arm_compute::Size2D dilation_2d(std::stoi(dilation[0]), std::stoi(dilation[1]));
+
+ // Collect inputs and outputs, handling both nn.conv2d and qnn.conv2d cases.
+ std::vector<JSONGraphNodeEntry> inputs = node.GetInputs();
+ size_t num_inputs = inputs.size();
+ bool has_bias;
+ if (node.GetOpName() == "qnn.depthwise_conv2d") {
+ CHECK(num_inputs >= 8U && num_inputs <= 9U)
+ << "Quantized convolution requires 9 inputs with a bias, 8 inputs without.";
+ has_bias = num_inputs == 9;
+ layer->inputs.push_back(MakeACLTensorFromJSONEntry(inputs[0], &inputs[4], &inputs[2]));
+ layer->inputs.push_back(MakeACLTensorFromJSONEntry(inputs[1], &inputs[5], &inputs[3]));
+ if (has_bias) {
+ layer->inputs.push_back(MakeACLTensorFromJSONEntry(inputs[6]));
+ }
+ layer->outputs.push_back(
+ MakeACLTensorFromJSONNode(node, &inputs[6 + has_bias], &inputs[7 + has_bias]));
+ } else {
+ CHECK(num_inputs >= 2U && num_inputs <= 3U)
Review comment:
Should be `ICHECK`?
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[GitHub] [tvm] mbaret commented on pull request #7206: [BYOC][ACL] Depthwise convolution support
Posted by GitBox <gi...@apache.org>.
mbaret commented on pull request #7206:
URL: https://github.com/apache/tvm/pull/7206#issuecomment-757985029
Thanks @d-smirnov @lhutton1 ! This is now merged.
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