[GitHub] [incubator-tvm] jackwish commented on a change in pull request #4351: [QNN] Lowering for Depthwise Convolution.

[GitBox <gi...@apache.org>]

jackwish commented on a change in pull request #4351: [QNN] Lowering for Depthwise Convolution.
URL: https://github.com/apache/incubator-tvm/pull/4351#discussion_r347756284
 
 

 ##########
 File path: src/relay/qnn/op/convolution.cc
 ##########
 @@ -417,23 +565,33 @@ Expr QnnConv2DCanonicalize(const Attrs& attrs, const Array<Expr>& new_args,
         param->kernel_layout == "HWOI")
       << "qnn.conv2d supports only OIHW/HWIO/HWOI kernel data layout.";
 
-  int batch_size, in_channels, out_channels, kernel_h, kernel_w;
-  std::tie(batch_size, in_channels, out_channels, kernel_h, kernel_w) =
+  int batch_size, in_channels, out_channels, kernel_h, kernel_w, channel_multiplier;
+  std::tie(batch_size, in_channels, out_channels, kernel_h, kernel_w, channel_multiplier) =
       GetWorkload(arg_types, param);
 
-  // Fallback to int32 conv if there is dilation or depthwise conv2d
+  // Fallback to int32 conv if there is dilation or grouped conv2d
+
   CHECK_EQ(param->dilation.size(), 2) << "qnn.conv2d only supports 2D dilation";
   auto dilation_h = get_const_int(param->dilation[0]);
   auto dilation_w = get_const_int(param->dilation[1]);
-  if (dilation_h != 1 || dilation_w != 1 || param->groups != 1) {
+  if (dilation_h != 1 || dilation_w != 1 || (param->groups != 1 && !is_depthwise(param))) {
     return Conv2DFallBack(data, weight, param);
+  } else if (is_depthwise(param)) {
+    CHECK_NE(channel_multiplier, -1);
+    auto padded_data = Conv2DPadInput(data, param);
+    auto term1 = Conv2DFirstTerm(padded_data, weight, param);
+    auto term2 =
+        DepthwiseConv2DSecondTerm(padded_data, param, kernel_h, kernel_w, channel_multiplier);
+    auto term3 = DepthwiseConv2DThirdTerm(weight, param, out_channels, channel_multiplier);
+    auto term4 = DepthwiseConv2DFourthTerm(param, kernel_h, kernel_w);
+    return Conv2DCombineTerms(term1, term2, term3, term4, param);
   }
 
   auto padded_data = Conv2DPadInput(data, param);
   auto term1 = Conv2DFirstTerm(padded_data, weight, param);
   auto term2 = Conv2DSecondTerm(padded_data, param, kernel_h, kernel_w, out_channels);
-  auto term3 = Conv2DThirdTerm(weight, param, batch_size, out_channels);
-  auto term4 = Conv2DFourthTerm(param, batch_size, in_channels, kernel_h, kernel_w);
+  auto term3 = Conv2DThirdTerm(weight, param, out_channels);
+  auto term4 = Conv2DFourthTerm(param, in_channels, kernel_h, kernel_w);
 
 Review comment:
   That is interesting, why removing batch semantic?

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