Add V0 tensor layout generation

include/ttmlir/Dialect/TTIR/Analysis/LegalGridAnalysis.h

@@ -15,6 +15,7 @@ struct LegalGridAnalysisInput {
   ChipDescAttr chipDesc;
   GridAttr maxGrid;
   RankedTensorType tensorType;
+  int64_t maxShardedGrids = 64;
   llvm::StringMap<SmallVector<int64_t, 2>> *gridSizeOverrides;
 
   LegalGridAnalysisInput()

lib/Dialect/TTIR/Analysis/LegalGridAnalysis.cpp

@@ -3,14 +3,48 @@
 // SPDX-License-Identifier: Apache-2.0
 
 #include "ttmlir/Dialect/TTIR/Analysis/LegalGridAnalysis.h"
+#include "ttmlir/Dialect/TTIR/IR/TTIROps.h"
 
 namespace mlir::tt::ttir {
 
+bool mock_is_output_tensor_legal_for_op(Operation *op, LayoutAttr layout) {
+  // Placeholder, needs to be replaced with a call the the TTNN op interface.
+  return true;
+}
+
+bool tensor_shape_compatible_with_shard(Operation *op, LayoutAttr layout) {
+  // These constraints are implemented seperatelly in every TTNN op.
+  // Almost nothing seems to be shared between EVERY op, so is hard to have any
+  // logic here without the risk of discarding a valid configuraiton or modeling
+  // the constraint for each op. This logic may be offloaded to the TTNN op
+  // interface.
+
+  // For now we will check if the tilised tensor dims are divisible by the grid
+  // dims. This will definitly discard possible valid configurations, but is a
+  // start.
+  RankedTensorType tensorType =
+      mlir::cast<RankedTensorType>(op->getResult(0).getType());
+  llvm::ArrayRef<int64_t> tensorShape = tensorType.getShape();
+
+  int64_t MTiles = 1;
+  if (tensorType.getRank() >= 2) {
+    MTiles = (tensorShape.rbegin()[1] + 31) / 32;
+  }
+
+  int64_t KTIles = (tensorShape.back() + 31) / 32;
+
+  int64_t gridR = layout.getGrid().getShape()[0];
+  int64_t gridC = layout.getGrid().getShape()[1];
+
+  return (MTiles % gridR == 0) && (KTIles % gridC == 0);
+}
+
 bool LegalGridAnalysis::applyOverrides() {
   // Lookup grid size overrides based on location information for current
   // operation.
   //
 
+  // TODO(odjuricic): Need to override all params, not just grid size.
   RankedTensorType tensorType =
       mlir::cast<RankedTensorType>(op->getResult(0).getType());
   LayoutAttr layout = mlir::cast<LayoutAttr>(tensorType.getEncoding());
@@ -36,17 +70,114 @@ bool LegalGridAnalysis::applyOverrides() {
 }
 
 void LegalGridAnalysis::analysisImplementation() {
-  // Placeholder, needs to be implemented. Go through all the grid sizes and
-  // check if they are legal based on tensor type and device/chip attributes.
-  // For now result of analysis is maximum supported grid size.
-  //
+  // A first incomplete implementation of the LegalGridAnalysis.
+  // This implementation is a placeholder and is meant to just enable testing of
+  // other components.
+
+  // Process only TTIR ops.
+  if (not llvm::isa<TTIROp>(op)) {
+    return;
+  }
+  // Skip operations that don't have output tensors.
+  if (op->getNumResults() == 0) {
+    return;
+  }
+  if (llvm::isa<ToLayoutOp>(op)) {
+    return;
+  }
+
+  // Get output tensor type.
   RankedTensorType tensorType =
       mlir::cast<RankedTensorType>(op->getResult(0).getType());
   LayoutAttr layout = mlir::cast<LayoutAttr>(tensorType.getEncoding());
-  llvm::ArrayRef<int64_t> tensorShape = tensorType.getShape();
 
-  analysisResult.push_back(layout.withGrid(
-      op->getContext(), tensorShape,
-      GridAttr::get(op->getContext(), analysisInput.maxGrid.getShape())));
+  // DRAM
+  // No grid is set since the tensor is not sharded.
+  // TODO(odjuricic): We need to set grid here since it will be used as the
+  // compute gird. (not implemented in runtime atm)
+  LayoutAttr dram =
+      layout.withMemorySpace(op->getContext(), MemorySpace::DeviceDRAM)
+          .withMemoryLayout(op->getContext(), TensorMemoryLayout::Interleaved)
+          .withGrid(op->getContext(), tensorType,
+                    GridAttr::get(op->getContext(),
+                                  analysisInput.maxGrid.getShape()));
+  if (mock_is_output_tensor_legal_for_op(op, dram)) {
+    analysisResult.push_back(dram);
+  }
+
+  // L1 Interleaved (same as above).
+  LayoutAttr l1Interleaved =
+      layout.withMemorySpace(op->getContext(), MemorySpace::DeviceL1)
+          .withMemoryLayout(op->getContext(), TensorMemoryLayout::Interleaved)
+          .withGrid(op->getContext(), tensorType,
+                    GridAttr::get(op->getContext(),
+                                  analysisInput.maxGrid.getShape()));
+  if (mock_is_output_tensor_legal_for_op(op, l1Interleaved)) {
+    analysisResult.push_back(l1Interleaved);
+  }
+
+  // L1 Sharded
+  LayoutAttr shardedBase =
+      layout.withMemorySpace(op->getContext(), MemorySpace::DeviceL1);
+  std::vector<LayoutAttr> shardedResults;
+
+  // Block Sharded
+  for (auto width = 1; width <= analysisInput.maxGrid.getShape()[0]; ++width) {
+    for (auto height = 1; height <= analysisInput.maxGrid.getShape()[1];
+         ++height) {
+      shardedResults.push_back(
+          shardedBase
+              .withGrid(op->getContext(), tensorType,
+                        GridAttr::get(op->getContext(), {width, height}))
+              .withMemoryLayout(op->getContext(),
+                                TensorMemoryLayout::BlockSharded));
+    }
+  }
+
+  auto numCores =
+      analysisInput.maxGrid.getShape()[0] * analysisInput.maxGrid.getShape()[1];
+  // Height Sharded
+  // TODO(odjuricic): Missing affine mapping to actual grid. Need to check with
+  // runtime implementation on what to produce here.
+  for (auto height = 2; height <= numCores; ++height) {
+    shardedResults.push_back(
+        shardedBase
+            .withGrid(op->getContext(), tensorType,
+                      GridAttr::get(op->getContext(), {height, 1}))
+            .withMemoryLayout(op->getContext(),
+                              TensorMemoryLayout::HeightSharded));
+  }
+
+  // Width Sharded
+  for (auto width = 2; width <= numCores; ++width) {
+    shardedResults.push_back(
+        shardedBase
+            .withGrid(op->getContext(), tensorType,
+                      GridAttr::get(op->getContext(), {1, width}))
+            .withMemoryLayout(op->getContext(),
+                              TensorMemoryLayout::WidthSharded));
+  }
+
+  // Filter layouts based on output tensor legality for current op.
+  shardedResults.erase(
+      std::remove_if(shardedResults.begin(), shardedResults.end(),
+                     [this](LayoutAttr layout) {
+                       return !tensor_shape_compatible_with_shard(op, layout) ||
+                              !mock_is_output_tensor_legal_for_op(op, layout);
+                     }),
+      shardedResults.end());
+
+  // Pick top largest sharded grids.
+  std::sort(shardedResults.begin(), shardedResults.end(),
+            [](LayoutAttr a, LayoutAttr b) {
+              return a.getGrid().getShape()[0] * a.getGrid().getShape()[1] >
+                     b.getGrid().getShape()[0] * b.getGrid().getShape()[1];
+            });
+
+  analysisResult.insert(
+      analysisResult.end(), shardedResults.begin(),
+      shardedResults.begin() +
+          std::min(analysisInput.maxShardedGrids,
+                   static_cast<int64_t>(shardedResults.size())));
 }
 } // namespace mlir::tt::ttir

lib/Dialect/TTIR/Analysis/OptimalTargetGridAnalysis.cpp

@@ -25,7 +25,9 @@ void OptimalTargetGridAnalysis::analysisImplementation() {
   // Placeholder: pick the first legal grid.
   //
   for (auto opGrids : analysisInput.legalGrids) {
-    analysisResult[opGrids.first] = opGrids.second[0];
+    if (not opGrids.second.empty()) {
+      analysisResult[opGrids.first] = opGrids.second[0];
+    }
   }
 }
 } // namespace mlir::tt::ttir

lib/Dialect/TTIR/Transforms/Passes.cpp

@@ -22,6 +22,7 @@
 #include "ttmlir/Dialect/TTIR/Analysis/OptimalTargetGridAnalysis.h"
 #include "ttmlir/Dialect/TTIR/Transforms/Passes.h"
 #include "ttmlir/Utils.h"
+#include <mlir/Interfaces/DestinationStyleOpInterface.h>
 
 namespace mlir::tt::ttir {
 #define GEN_PASS_DEF_TTIRGENERICKERNEL
@@ -1095,9 +1096,18 @@ class TTIRGridSet : public impl::TTIRGridSetBase<TTIRGridSet> {
 
         // Update the output layout attribute with the new grid size.
         //
-        op->getResult(0).setType(RankedTensorType::get(
-            tensorShape, tensorType.getElementType(),
-            optimalTargetGridAnalysis.getResult().at(op)));
+        if (optimalTargetGridAnalysis.getResult().contains(op)) {
+          RankedTensorType newTensorType = RankedTensorType::get(
+              tensorShape, tensorType.getElementType(),
+              optimalTargetGridAnalysis.getResult().at(op));
+
+          op->getResult(0).setType(newTensorType);
+
+          if (llvm::isa<mlir::DestinationStyleOpInterface>(op)) {
+            // Update dps operand layout as well.
+            op->getOperands().back().setType(newTensorType);
+          }
+        }
       });
 
       // Update the function type to reflect the updated return operation's

test/ttmlir/Dialect/TTIR/test_grid_set.mlir

@@ -3,8 +3,8 @@
 module attributes {} {
   func.func @forward(%arg0: tensor<64x128xf32>, %arg1: tensor<64x128xf32>) -> tensor<64x128xf32> {
     %0 = tensor.empty() : tensor<64x128xf32>
-    // CHECK: #[[LAYOUT_1:.*]] = #tt.layout<(d0, d1) -> (d0, d1), undef, <8x8>, memref<8x16xf32, #dram>, interleaved>
-    // CHECK: %[[C:.*]] = "ttir.multiply"[[C:.*]] -> tensor<64x128xf32, #[[LAYOUT_1]]>
+    // CHECK: #[[LAYOUT_2:.*]] = #tt.layout<(d0, d1) -> (d0, d1), undef, <8x8>, memref<8x16xf32, #dram>, interleaved>
+    // CHECK: %[[C:.*]] = "ttir.multiply"[[C:.*]] -> tensor<64x128xf32, #[[LAYOUT_2]]>
     %1 = "ttir.multiply"(%arg0, %arg1, %0) <{operandSegmentSizes = array<i32: 2, 1>, operand_constraints = [#any_device, #any_device, #any_device]}> : (tensor<64x128xf32>, tensor<64x128xf32>, tensor<64x128xf32>) -> tensor<64x128xf32>
     return %1 : tensor<64x128xf32>
   }

test/ttmlir/Dialect/TTNN/multiple_add_with_loc.mlir

@@ -3,17 +3,17 @@
 #loc = loc("test_ops.py:17_0_0":0:0)
 module attributes {} {
   func.func @main(%arg0: tensor<1x32x32xf32> loc("test_ops.py:17_0_0":0:0), %arg1: tensor<1x32x32xf32> loc("test_ops.py:17_0_0":0:0), %arg2: tensor<1x32x32xf32> loc("test_ops.py:17_0_0":0:0)) -> (tensor<1x32x32xf32>, tensor<1x32x32xf32>) {
-    // CHECK: #[[LAYOUT_1:.*]] = #tt.layout<(d0, d1, d2) -> (d0 * 32 + d1, d2), undef, <8x8>, memref<4x4xf32, #dram>, interleaved>
+    // CHECK: #[[LAYOUT_2:.*]] = #tt.layout<(d0, d1, d2) -> (d0 * 32 + d1, d2), undef, <8x8>, memref<4x4xf32, #dram>, interleaved>
     %0 = tensor.empty() : tensor<1x32x32xf32> loc(#loc5)
-    // CHECK: %[[C:.*]] = "ttnn.add"[[C:.*]] -> tensor<1x32x32xf32, #[[LAYOUT_1]]>
+    // CHECK: %[[C:.*]] = "ttnn.add"[[C:.*]] -> tensor<1x32x32xf32, #[[LAYOUT_2]]>
     %1 = "ttir.add"(%arg1, %arg2, %0) <{operandSegmentSizes = array<i32: 2, 1>, operand_constraints = [#any_device, #any_device, #any_device]}> : (tensor<1x32x32xf32>, tensor<1x32x32xf32>, tensor<1x32x32xf32>) -> tensor<1x32x32xf32> loc(#loc5)
     %2 = tensor.empty() : tensor<1x32x32xf32> loc(#loc6)
-    // CHECK: %[[C:.*]] = "ttnn.add"[[C:.*]] -> tensor<1x32x32xf32, #[[LAYOUT_1]]>
+    // CHECK: %[[C:.*]] = "ttnn.add"[[C:.*]] -> tensor<1x32x32xf32, #[[LAYOUT_2]]>
     %3 = "ttir.add"(%1, %arg0, %2) <{operandSegmentSizes = array<i32: 2, 1>, operand_constraints = [#any_device, #any_device, #any_device]}> : (tensor<1x32x32xf32>, tensor<1x32x32xf32>, tensor<1x32x32xf32>) -> tensor<1x32x32xf32> loc(#loc6)
     %4 = tensor.empty() : tensor<1x32x32xf32> loc(#loc7)
-    // CHECK: %[[C:.*]] = "ttnn.add"[[C:.*]] -> tensor<1x32x32xf32, #[[LAYOUT_1]]>
+    // CHECK: %[[C:.*]] = "ttnn.add"[[C:.*]] -> tensor<1x32x32xf32, #[[LAYOUT_2]]>
     %5 = "ttir.add"(%arg2, %arg1, %4) <{operandSegmentSizes = array<i32: 2, 1>, operand_constraints = [#any_device, #any_device, #any_device]}> : (tensor<1x32x32xf32>, tensor<1x32x32xf32>, tensor<1x32x32xf32>) -> tensor<1x32x32xf32> loc(#loc7)
-    // CHECK: return %[[R0:.*]], %[[R1:.*]] : tensor<1x32x32xf32, #layout1>, tensor<1x32x32xf32, #layout1>
+    // CHECK: return %[[R0:.*]], %[[R1:.*]] : tensor<1x32x32xf32, #layout>, tensor<1x32x32xf32, #layout>
     return %3, %5 : tensor<1x32x32xf32>, tensor<1x32x32xf32> loc(#loc4)
   } loc(#loc)
 } loc(#loc)

test/ttmlir/Dialect/TTNN/multiple_add_with_loc_grid_override.mlir

@@ -3,17 +3,17 @@
 #loc = loc("test_ops.py:17_0_0":0:0)
 module attributes {} {
   func.func @main(%arg0: tensor<1x32x32xf32> loc("test_ops.py:17_0_0":0:0), %arg1: tensor<1x32x32xf32> loc("test_ops.py:17_0_0":0:0), %arg2: tensor<1x32x32xf32> loc("test_ops.py:17_0_0":0:0)) -> (tensor<1x32x32xf32>, tensor<1x32x32xf32>) {
-    // CHECK: #[[LAYOUT_0:.*]] = #tt.layout<(d0, d1, d2) -> (d0 * 32 + d1, d2), undef, <8x8>, memref<4x4xf32, #system>>
+    // CHECK: #[[LAYOUT_0:.*]] = #tt.layout<(d0, d1, d2) -> (d0 * 32 + d1, d2), undef, <1x1>, memref<32x32xf32, #system>>
     // CHECK: #[[LAYOUT_1:.*]] = #tt.layout<(d0, d1, d2) -> (d0 * 32 + d1, d2), undef, <4x4>, memref<8x8xf32, #dram>, interleaved>
-    // CHECK: #[[LAYOUT_2:.*]] = #tt.layout<(d0, d1, d2) -> (d0 * 32 + d1, d2), undef, <8x8>, memref<4x4xf32, #dram>, interleaved>
+    // CHECK: #[[LAYOUT_3:.*]] = #tt.layout<(d0, d1, d2) -> (d0 * 32 + d1, d2), undef, <8x8>, memref<4x4xf32, #dram>, interleaved>
     %0 = tensor.empty() : tensor<1x32x32xf32> loc(#loc5)
     // CHECK: %[[C:.*]] = "ttnn.add"[[C:.*]] -> tensor<1x32x32xf32, #[[LAYOUT_1]]>
     %1 = "ttir.add"(%arg1, %arg2, %0) <{operandSegmentSizes = array<i32: 2, 1>, operand_constraints = [#any_device, #any_device, #any_device]}> : (tensor<1x32x32xf32>, tensor<1x32x32xf32>, tensor<1x32x32xf32>) -> tensor<1x32x32xf32> loc(#loc5)
     %2 = tensor.empty() : tensor<1x32x32xf32> loc(#loc6)
     // CHECK: %[[C:.*]] = "ttnn.add"[[C:.*]] -> tensor<1x32x32xf32, #[[LAYOUT_1]]>
     %3 = "ttir.add"(%1, %arg0, %2) <{operandSegmentSizes = array<i32: 2, 1>, operand_constraints = [#any_device, #any_device, #any_device]}> : (tensor<1x32x32xf32>, tensor<1x32x32xf32>, tensor<1x32x32xf32>) -> tensor<1x32x32xf32> loc(#loc6)
     %4 = tensor.empty() : tensor<1x32x32xf32> loc(#loc7)
-    // CHECK: %[[C:.*]] = "ttnn.add"[[C:.*]] -> tensor<1x32x32xf32, #[[LAYOUT_2]]>
+    // CHECK: %[[C:.*]] = "ttnn.add"[[C:.*]] -> tensor<1x32x32xf32, #[[LAYOUT_3]]>
     %5 = "ttir.add"(%arg2, %arg1, %4) <{operandSegmentSizes = array<i32: 2, 1>, operand_constraints = [#any_device, #any_device, #any_device]}> : (tensor<1x32x32xf32>, tensor<1x32x32xf32>, tensor<1x32x32xf32>) -> tensor<1x32x32xf32> loc(#loc7)
     // CHECK: return %[[R0:.*]], %[[R1:.*]] : tensor<1x32x32xf32, #[[LAYOUT_0]]>, tensor<1x32x32xf32, #[[LAYOUT_0]]>
     return %3, %5 : tensor<1x32x32xf32>, tensor<1x32x32xf32> loc(#loc4)

test/ttmlir/Dialect/TTNN/ttir_to_ttnn_pipeline.mlir

@@ -2,11 +2,11 @@
 #any_device = #tt.operand_constraint<dram|l1|scalar|tile|any_device|any_device_tile>
 module attributes {} {
   func.func @forward(%arg0: tensor<64x128xf32>, %arg1: tensor<64x128xf32>) -> tensor<64x128xf32> {
-    // CHECK: #[[LAYOUT_1:.*]] = #tt.layout<(d0, d1) -> (d0, d1), undef, <8x8>, memref<8x16xf32, #dram>, interleaved>
+    // CHECK: #[[LAYOUT_2:.*]] = #tt.layout<(d0, d1) -> (d0, d1), undef, <8x8>, memref<8x16xf32, #dram>, interleaved>
     // CHECK: %[[C:.*]] = "ttnn.open_device"[[C:.*]]
     // CHECK: %[[C:.*]] = "ttnn.empty"[[C:.*]]
     %0 = tensor.empty() : tensor<64x128xf32>
-    // CHECK: %[[C:.*]] = "ttnn.multiply"[[C:.*]] -> tensor<64x128xf32, #[[LAYOUT_1]]>
+    // CHECK: %[[C:.*]] = "ttnn.multiply"[[C:.*]] -> tensor<64x128xf32, #[[LAYOUT_2]]>
     %1 = "ttir.multiply"(%arg0, %arg1, %0) <{operandSegmentSizes = array<i32: 2, 1>, operand_constraints = [#any_device, #any_device, #any_device]}> : (tensor<64x128xf32>, tensor<64x128xf32>, tensor<64x128xf32>) -> tensor<64x128xf32>
     // CHECK: "ttnn.close_device"[[C:.*]]
     return %1 : tensor<64x128xf32>