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1D Convolution where input spatial dimension isn't 2 #2358

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1D Convolution where input spatial dimension isn't 2 #2358

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5 changes: 0 additions & 5 deletions lib/Conversion/TTIRToTTIRDecomposition/TTIRToTTIRDecomposition.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -213,11 +213,6 @@ struct Legalize1DConvolutionPattern : public ConvolutionDecompositionPattern {
return failure();
}

// Not currently supporting spatial dims other than 2 for the 1D case.
if (op.getConvolutionLayout().getInputSpatialDimensions()[0] != 2) {
return failure();
}

// The shapes that the convolution currently operates with have are 3D, and
// we need to add another dimension for it to match the conv2d signature, so
// adding a dimension of size 1 to the end of input and output shapes.
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22 changes: 21 additions & 1 deletion test/ttmlir/Dialect/TTNN/convolution/simple_conv1d.mlir
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@@ -1,6 +1,6 @@
// RUN: ttmlir-opt --ttir-to-ttnn-backend-pipeline %s | FileCheck %s
module {
func.func @main(%arg0: tensor<1x256x512xf32>, %arg1: tensor<1024x256x1xf32>, %arg2: tensor<1024xf32>) -> tensor<1x1024x512xf32> {
func.func @conv1d_test1(%arg0: tensor<1x256x512xf32>, %arg1: tensor<1024x256x1xf32>, %arg2: tensor<1024xf32>) -> tensor<1x1024x512xf32> {
%0 = tensor.empty() : tensor<1x1024x512xf32>
// CHECK: "ttnn.reshape"
// CHECK-SAME: shape = [1 : i32, 256 : i32, 512 : i32, 1 : i32]
Expand All @@ -17,4 +17,24 @@ module {
// CHECK: return %{{.*}} : tensor<1x1024x512xf32, #ttnn_layout3>
return %1 : tensor<1x1024x512xf32>
}

func.func public @conv1d_test2(%arg0: tensor<1x7x768xbf16>, %arg1: tensor<1x192x768xbf16>) -> (tensor<1x7x768xbf16>) {
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Let's just put some more descriptive test names than test1 and test2, or write some comment above each test explaining the case it is testing :)

%0 = tensor.empty() : tensor<1x7x768xbf16>
// CHECK: "ttnn.reshape"
// CHECK-SAME: shape = [1 : i32, 7 : i32, 768 : i32, 1 : i32]
// CHECK: "ttnn.reshape"
// CHECK-SAME: shape = [1 : i32, 192 : i32, 768 : i32, 1 : i32]
// CHECK: "ttnn.permute"
// CHECK-SAME: permutation = array<i64: 0, 1, 3, 2>
// CHECK: "ttnn.permute"
// CHECK-SAME: permutation = array<i64: 2, 1, 0, 3>
// CHECK: "ttnn.conv2d"
// CHECK: "ttnn.permute"
// CHECK-SAME: permutation = array<i64: 0, 1, 3, 2>
// CHECK: "ttnn.reshape"
// CHECK-SAME: shape = [1 : i32, 7 : i32, 768 : i32]
%1 = "ttir.convolution"(%arg0, %arg1, %0) <{batch_group_count = 1 : i64, convolution_layout = #ttir<convolution_layout input_batch = 0, input_feature = 2, input_spatial_dimensions = 1, kernel_output_feature = 2, kernel_input_feature = 1, kernel_spatial_dimensions = 0, output_batch = 0, output_feature = 2, output_spatial_dimensions = 1>, feature_group_count = 4 : i64, input_dilation = array<i64: 1>, padding = array<i64: 0, 0>, weight_dilation = array<i64: 1>, window_reversal = array<i1: false>, window_strides = array<i64: 1>}> : (tensor<1x7x768xbf16>, tensor<1x192x768xbf16>, tensor<1x7x768xbf16>) -> tensor<1x7x768xbf16>
// CHECK: return %{{.*}} : tensor<1x7x768xbf16, #ttnn_layout{{.*}}>
return %1 : tensor<1x7x768xbf16>
}
}
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