From 29a1e20b585ce0d5f4950bc1819792730f9b9ca5 Mon Sep 17 00:00:00 2001
From: Phil Tillet <phil@openai.com>
Date: Wed, 4 Jan 2023 17:02:31 -0800
Subject: [PATCH] tweak convert + trans

---
 python/bwd.ttgir | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/python/bwd.ttgir b/python/bwd.ttgir
index 5ac66c050..5ac495943 100644
--- a/python/bwd.ttgir
+++ b/python/bwd.ttgir
@@ -81,13 +81,13 @@ module attributes {"triton_gpu.num-warps" = 8 : i32} {
       %63 = tt.addptr %30, %62 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
       %64 = tt.load %63 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked1>
       %65 = arith.index_cast %47 : i32 to index
-      %66 = triton_gpu.convert_layout %59 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared0>
-      %67 = tt.trans %66 : (tensor<128x64xf16, #shared0>) -> tensor<64x128xf16, #shared1>
+      %66 = triton_gpu.convert_layout %59 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared1>
+      %67 = tt.trans %66 : (tensor<128x64xf16, #shared1>) -> tensor<64x128xf16, #shared0>
       %68 = arith.addi %49, %16 : tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #mma0}>>
       %69 = tt.expand_dims %68 {axis = 0 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #mma0}>>) -> tensor<1x128xi32, #mma0>
       %70 = tt.broadcast %69 : (tensor<1x128xi32, #mma0>) -> tensor<128x128xi32, #mma0>
-      %71 = triton_gpu.convert_layout %64 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared0>
-      %72 = tt.trans %71 : (tensor<128x64xf16, #shared0>) -> tensor<64x128xf16, #shared1>
+      %71 = triton_gpu.convert_layout %64 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared1>
+      %72 = tt.trans %71 : (tensor<128x64xf16, #shared1>) -> tensor<64x128xf16, #shared0>
       %73 = arith.muli %54, %20 : tensor<128x1xi32, #blocked2>
       %74 = tt.broadcast %73 : (tensor<128x1xi32, #blocked2>) -> tensor<128x64xi32, #blocked2>
       %75 = arith.addi %74, %26 : tensor<128x64xi32, #blocked2>
@@ -102,7 +102,7 @@ module attributes {"triton_gpu.num-warps" = 8 : i32} {
         %900 = tt.load %arg30 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked1>
         %90 = triton_gpu.convert_layout %900 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared1>
         %92 = triton_gpu.convert_layout %90 : (tensor<128x64xf16, #shared1>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>>
-        %91 = triton_gpu.convert_layout %67 : (tensor<64x128xf16, #shared1>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>>
+        %91 = triton_gpu.convert_layout %67 : (tensor<64x128xf16, #shared0>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>>
         %93 = tt.dot %92, %91, %cst_0 {allowTF32 = true} : tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>> * tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>> -> tensor<128x128xf32, #mma0>
         %94 = arith.addi %88, %18 : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>
         %95 = tt.expand_dims %94 {axis = 1 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>) -> tensor<128x1xi32, #mma0>
@@ -132,7 +132,7 @@ module attributes {"triton_gpu.num-warps" = 8 : i32} {
         %118 = tt.broadcast %117 : (tensor<128x1xf32, #mma0>) -> tensor<128x128xf32, #mma0>
         %119 = arith.subf %cst_0, %118 : tensor<128x128xf32, #mma0>
         %120 = triton_gpu.convert_layout %107 : (tensor<128x64xf16, #shared1>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>>
-        %121 = triton_gpu.convert_layout %72 : (tensor<64x128xf16, #shared1>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>>
+        %121 = triton_gpu.convert_layout %72 : (tensor<64x128xf16, #shared0>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>>
         %122 = tt.dot %120, %121, %119 {allowTF32 = true} : tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>> * tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>> -> tensor<128x128xf32, #mma0>
         %123 = arith.mulf %106, %122 : tensor<128x128xf32, #mma0>
         %124 = arith.mulf %123, %39 : tensor<128x128xf32, #mma0>
@@ -145,7 +145,7 @@ module attributes {"triton_gpu.num-warps" = 8 : i32} {
         %131 = tt.load %arg29 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf32, #blocked2>
         %132 = triton_gpu.convert_layout %131 : (tensor<128x64xf32, #blocked2>) -> tensor<128x64xf32, #mma1>
         %133 = triton_gpu.convert_layout %126 : (tensor<128x128xf16, #shared1>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>>
-        %134 = triton_gpu.convert_layout %66 : (tensor<128x64xf16, #shared0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
+        %134 = triton_gpu.convert_layout %66 : (tensor<128x64xf16, #shared1>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
         %135 = tt.dot %133, %134, %132 {allowTF32 = true} : tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>> * tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>> -> tensor<128x64xf32, #mma1>
         %136 = triton_gpu.convert_layout %135 : (tensor<128x64xf32, #mma1>) -> tensor<128x64xf32, #blocked2>
         tt.store %arg29, %136 : tensor<128x64xf32, #blocked2>