mirror of https://github.com/llvm/torch-mlir
6678e1a256
Before this PR, a statically shaped aten.convolution would generate dynamically shaped linalg IR, and even `-canonicalize` would not be able to fold it back into static shapes. This PR ensure that shape calculations are folded on construction to directly generate statically shaped linalg IR. We achieve that by ensuring that `arith` ops involved in computing shapes are created via `createOrFold`, so that later uses of `getAsOpFoldResult` see constants instead of those ops. For example ``` module { func.func @forward(%arg0: !torch.vtensor<[32,336,112,112],f32>, %arg1: !torch.vtensor<[336,168,3,3],f32>, %arg2: !torch.vtensor<[336],f32>) -> !torch.vtensor<[32,336,56,56],f32> { %false = torch.constant.bool false %int2 = torch.constant.int 2 %int1 = torch.constant.int 1 %0 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int> %1 = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int> %2 = torch.prim.ListConstruct : () -> !torch.list<int> %3 = torch.aten.convolution %arg0, %arg1, %arg2, %1, %0, %0, %false, %2, %int2 : !torch.vtensor<[32,336,112,112],f32>, !torch.vtensor<[336,168,3,3],f32>, !torch.vtensor<[336],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[32,336,56,56],f32> return %3 : !torch.vtensor<[32,336,56,56],f32> } } ``` would result in ``` [...] %padded = tensor.pad %2 low[%14, %15, %16, %17] high[%14, %15, %16, %17] { ^bb0(%arg3: index, %arg4: index, %arg5: index, %arg6: index): tensor.yield %cst : f32 } : tensor<32x336x112x112xf32> to tensor<?x?x?x?xf32> [...] %45 = linalg.conv_2d_ngchw_gfchw {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>} ins(%expanded, %expanded_37 : tensor<?x2x?x?x?xf32>, tensor<2x168x168x3x3xf32>) outs(%expanded_44 : tensor<32x2x168x?x?xf32>) -> tensor<32x2x168x?x?xf32> [...] ``` and with this PR all shapes are static. |
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CMakeLists.txt | ||
Utils.cpp |