more tests

test/TritonGPU/loop-pipeline.mlir

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+// RUN: triton-opt %s -tritongpu-pipeline=num-stages=3
+// RUN: triton-opt %s -tritongpu-pipeline=num=stages=3 -tritongpu-verifier
+
+// 4 warps
+#AL = #triton_gpu.blocked_layout<{
+  threadTileSize = [1, 4],
+  warpTileSize = [4, 32],
+  blockTileSize = [16, 32],
+  order = [1, 0]
+}>
+
+#BL = #triton_gpu.blocked_layout<{
+  threadTileSize = [1, 4],
+  warpTileSize = [1, 128],
+  blockTileSize = [4, 128],
+  order = [1, 0]
+}>
+
+#A = #triton_gpu.shared_layout<{
+  vec = 2,
+  perPhase = 2,
+  maxPhase = 4,
+  order = [1, 0]
+}>
+
+#B = #triton_gpu.shared_layout<{
+  vec = 2,
+  perPhase = 2,
+  maxPhase = 4,
+  order = [1, 0]
+}>
+
+// TODO: check this
+#C = #triton_gpu.mma_layout<{
+  fragmentPerWarp = [1, 1],
+  shapePerWarp = [16, 8],
+  warpPerTile = [2, 2],
+  shapePerTile = [32, 16],
+  repetitions = [4, 4],
+  contigPerThread = [1, 8]
+}>
+
+// matmul: 128x32 @ 32x128 -> 128x128
+func @matmul_loop(%lb : index, %ub : index, %step : index, %A : !tt.ptr<f16>, %B : !tt.ptr<f16>) {
+  %a_ptr_init = tt.broadcast %A : (!tt.ptr<f16>) -> tensor<128x32x!tt.ptr<f16>, #AL>
+  %b_ptr_init = tt.broadcast %B : (!tt.ptr<f16>) -> tensor<32x128x!tt.ptr<f16>, #BL>
+
+  %a_mask = arith.constant dense<true> : tensor<128x32xi1, #AL>
+  %a_other = arith.constant dense<0.00e+00> : tensor<128x32xf16, #AL>
+  %b_mask = arith.constant dense<true> : tensor<32x128xi1, #BL>
+  %b_other = arith.constant dense<0.00e+00> : tensor<32x128xf16, #BL>
+  %c_init = arith.constant dense<0.00e+00> : tensor<128x128xf32, #C>
+
+  %a_off = arith.constant dense<4> : tensor<128x32xi32, #AL>
+  %b_off = arith.constant dense<4> : tensor<32x128xi32, #BL>
+
+  scf.for %iv = %lb to %ub step %step iter_args(%a_ptr = %a_ptr_init, %b_ptr = %b_ptr_init, %prev_c = %c_init) -> (tensor<128x32x!tt.ptr<f16>, #AL>, tensor<32x128x!tt.ptr<f16>, #BL>, tensor<128x128xf32, #C>) {
+    %a_ = tt.load %a_ptr, %a_mask, %a_other {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x32xf16, #AL>
+    %a = triton_gpu.convert_layout %a_ : (tensor<128x32xf16, #AL>) -> tensor<128x32xf16, #A>
+    %b_ = tt.load %b_ptr, %b_mask, %b_other {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<32x128xf16, #BL>
+    %b = triton_gpu.convert_layout %b_ : (tensor<32x128xf16, #BL>) -> tensor<32x128xf16, #B>
+
+    %c = tt.dot %a, %b, %prev_c {allowTF32 = true} : tensor<128x32xf16, #A> * tensor<32x128xf16, #B> -> tensor<128x128xf32, #C>
+    // %c = tt.dot %a_, %b_, %prev_c {allowTF32 = true} : tensor<128x32xf16, #AL> * tensor<32x128xf16, #BL> -> tensor<128x128xf32, #C>
+
+    %next_a_ptr = tt.getelementptr %a_ptr, %a_off : tensor<128x32x!tt.ptr<f16>, #AL>
+    %next_b_ptr = tt.getelementptr %b_ptr, %b_off : tensor<32x128x!tt.ptr<f16>, #BL>
+    scf.yield %next_a_ptr, %next_b_ptr, %c : tensor<128x32x!tt.ptr<f16>, #AL>, tensor<32x128x!tt.ptr<f16>, #BL>, tensor<128x128xf32, #C>
+  }
+  return
+}
+
+
+// nested loop

test/TritonGPU/ops.mlir

@@ -0,0 +1,5 @@
+// RUN: triton-opt %s -tritongpu-verifier
+func @test_dot(%a : tensor<128x32xf16>, %b : tensor<32x128xf16>, %c : tensor<128x128xf16>) {
+  %d = tt.dot %a, %b, %c {allowTF32 = true} : tensor<128x32xf16> * tensor<32x128xf16> -> tensor<128x128xf16>
+  return
+}