gemini-code-assist[bot] commented on code in PR #19523:
URL: https://github.com/apache/tvm/pull/19523#discussion_r3211131961
##########
python/tvm/relax/frontend/tflite/tflite_frontend.py:
##########
@@ -2449,6 +2450,143 @@ def convert_conv(self, op, conv_type):
out = self.convert_fused_activation_function(out,
fused_activation_fn)
return out
+ def convert_conv3d(self, op):
+ """3D convolution implementation."""
+
+ from tflite.BuiltinOptions import BuiltinOptions
+ from tflite.Conv3DOptions import Conv3DOptions
+ from tflite.Padding import Padding
+ from tflite.TensorType import TensorType
+
+ input_tensors = self.get_input_tensors(op)
+ assert len(input_tensors) >= 2, "input tensors length should be >= 2"
+
+ input_tensor = input_tensors[0]
+ input_tensor_idx = input_tensor.tensor_idx
+ weight_tensor = input_tensors[1]
+
+ output_tensors = self.get_output_tensors(op)
+ assert len(output_tensors) == 1, "output tensors length should be 1"
+ output_tensor = output_tensors[0]
+
+ assert op.BuiltinOptionsType() == BuiltinOptions.Conv3DOptions
+ op_options = op.BuiltinOptions()
+ conv3d_options = Conv3DOptions()
+ conv3d_options.Init(op_options.Bytes, op_options.Pos)
+
+ stride_d = conv3d_options.StrideD()
+ stride_h = conv3d_options.StrideH()
+ stride_w = conv3d_options.StrideW()
+ dilation_d = conv3d_options.DilationDFactor()
+ dilation_h = conv3d_options.DilationHFactor()
+ dilation_w = conv3d_options.DilationWFactor()
+ padding = conv3d_options.Padding()
+ fused_activation_fn = conv3d_options.FusedActivationFunction()
+
+ _, input_d, input_h, input_w, input_c =
to_int_list(self.get_tensor_shape(input_tensor))
+ # TFLite Conv3D kernel layout is already DHWIO:
+ # KD KH KW IC OC
+ kernel_d, kernel_h, kernel_w, in_channels, output_channels =
to_int_list(
+ self.get_tensor_shape(weight_tensor)
+ )
+
+ dilated_kernel_d = dilation_d * (kernel_d - 1) + 1
+ dilated_kernel_h = dilation_h * (kernel_h - 1) + 1
+ dilated_kernel_w = dilation_w * (kernel_w - 1) + 1
+
+ params = {
+ # "kernel_size": [kernel_d, kernel_h, kernel_w],
+ "strides": [stride_d, stride_h, stride_w],
+ "dilation": [dilation_d, dilation_h, dilation_w],
+ "padding": [0, 0, 0],
+ "data_layout": "NDHWC",
+ }
+
+ params["kernel_layout"] = "DHWIO"
+ if input_c != in_channels:
+ assert input_c % in_channels == 0, (
+ "Input channels is not divisible of kernel in_channels."
Review Comment:
Typo in the error message: "divisible of" should be "divisible by".
```suggestion
"Input channels is not divisible by kernel in_channels."
```
##########
python/tvm/relax/frontend/tflite/tflite_frontend.py:
##########
@@ -2449,6 +2450,143 @@ def convert_conv(self, op, conv_type):
out = self.convert_fused_activation_function(out,
fused_activation_fn)
return out
+ def convert_conv3d(self, op):
+ """3D convolution implementation."""
+
+ from tflite.BuiltinOptions import BuiltinOptions
+ from tflite.Conv3DOptions import Conv3DOptions
+ from tflite.Padding import Padding
+ from tflite.TensorType import TensorType
+
+ input_tensors = self.get_input_tensors(op)
+ assert len(input_tensors) >= 2, "input tensors length should be >= 2"
+
+ input_tensor = input_tensors[0]
+ input_tensor_idx = input_tensor.tensor_idx
+ weight_tensor = input_tensors[1]
+
+ output_tensors = self.get_output_tensors(op)
+ assert len(output_tensors) == 1, "output tensors length should be 1"
+ output_tensor = output_tensors[0]
+
+ assert op.BuiltinOptionsType() == BuiltinOptions.Conv3DOptions
+ op_options = op.BuiltinOptions()
+ conv3d_options = Conv3DOptions()
+ conv3d_options.Init(op_options.Bytes, op_options.Pos)
+
+ stride_d = conv3d_options.StrideD()
+ stride_h = conv3d_options.StrideH()
+ stride_w = conv3d_options.StrideW()
+ dilation_d = conv3d_options.DilationDFactor()
+ dilation_h = conv3d_options.DilationHFactor()
+ dilation_w = conv3d_options.DilationWFactor()
+ padding = conv3d_options.Padding()
+ fused_activation_fn = conv3d_options.FusedActivationFunction()
+
+ _, input_d, input_h, input_w, input_c =
to_int_list(self.get_tensor_shape(input_tensor))
+ # TFLite Conv3D kernel layout is already DHWIO:
+ # KD KH KW IC OC
+ kernel_d, kernel_h, kernel_w, in_channels, output_channels =
to_int_list(
+ self.get_tensor_shape(weight_tensor)
+ )
+
+ dilated_kernel_d = dilation_d * (kernel_d - 1) + 1
+ dilated_kernel_h = dilation_h * (kernel_h - 1) + 1
+ dilated_kernel_w = dilation_w * (kernel_w - 1) + 1
+
+ params = {
+ # "kernel_size": [kernel_d, kernel_h, kernel_w],
+ "strides": [stride_d, stride_h, stride_w],
Review Comment:
Remove the commented-out code to keep the implementation clean and
maintainable.
```suggestion
"strides": [stride_d, stride_h, stride_w],
```
##########
python/tvm/relax/frontend/tflite/tflite_frontend.py:
##########
@@ -2449,6 +2450,143 @@ def convert_conv(self, op, conv_type):
out = self.convert_fused_activation_function(out,
fused_activation_fn)
return out
+ def convert_conv3d(self, op):
+ """3D convolution implementation."""
+
+ from tflite.BuiltinOptions import BuiltinOptions
+ from tflite.Conv3DOptions import Conv3DOptions
+ from tflite.Padding import Padding
+ from tflite.TensorType import TensorType
+
+ input_tensors = self.get_input_tensors(op)
+ assert len(input_tensors) >= 2, "input tensors length should be >= 2"
+
+ input_tensor = input_tensors[0]
+ input_tensor_idx = input_tensor.tensor_idx
+ weight_tensor = input_tensors[1]
+
+ output_tensors = self.get_output_tensors(op)
+ assert len(output_tensors) == 1, "output tensors length should be 1"
+ output_tensor = output_tensors[0]
+
+ assert op.BuiltinOptionsType() == BuiltinOptions.Conv3DOptions
+ op_options = op.BuiltinOptions()
+ conv3d_options = Conv3DOptions()
+ conv3d_options.Init(op_options.Bytes, op_options.Pos)
+
+ stride_d = conv3d_options.StrideD()
+ stride_h = conv3d_options.StrideH()
+ stride_w = conv3d_options.StrideW()
+ dilation_d = conv3d_options.DilationDFactor()
+ dilation_h = conv3d_options.DilationHFactor()
+ dilation_w = conv3d_options.DilationWFactor()
+ padding = conv3d_options.Padding()
+ fused_activation_fn = conv3d_options.FusedActivationFunction()
+
+ _, input_d, input_h, input_w, input_c =
to_int_list(self.get_tensor_shape(input_tensor))
+ # TFLite Conv3D kernel layout is already DHWIO:
+ # KD KH KW IC OC
+ kernel_d, kernel_h, kernel_w, in_channels, output_channels =
to_int_list(
+ self.get_tensor_shape(weight_tensor)
+ )
+
+ dilated_kernel_d = dilation_d * (kernel_d - 1) + 1
+ dilated_kernel_h = dilation_h * (kernel_h - 1) + 1
+ dilated_kernel_w = dilation_w * (kernel_w - 1) + 1
+
+ params = {
+ # "kernel_size": [kernel_d, kernel_h, kernel_w],
+ "strides": [stride_d, stride_h, stride_w],
+ "dilation": [dilation_d, dilation_h, dilation_w],
+ "padding": [0, 0, 0],
Review Comment:
For 3D convolution, it is more consistent to use a 6-element padding list
(front, top, left, back, bottom, right) even for the zero-padding case. This
aligns with the 6-element format used in the `SAME` padding logic and matches
the expected IR structure in the unit tests.
```suggestion
"padding": [0, 0, 0, 0, 0, 0],
```
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