[Mlir-commits] [mlir] [XeGPU] Add sg_map for scatter verification (PR #124300)

Artem Kroviakov llvmlistbot at llvm.org
Tue Jan 28 05:27:40 PST 2025


https://github.com/akroviakov updated https://github.com/llvm/llvm-project/pull/124300

>From 64348e0de8de48d32680767a7cb214a3f5a4e52c Mon Sep 17 00:00:00 2001
From: Artem Kroviakov <artem.kroviakov at intel.com>
Date: Tue, 28 Jan 2025 13:27:18 +0000
Subject: [PATCH] [XeGPU] Add sg_map for scatter verification

---
 .../include/mlir/Dialect/XeGPU/IR/XeGPUOps.td |  7 +--
 mlir/lib/Dialect/XeGPU/IR/XeGPUOps.cpp        | 37 ++++++++++-
 mlir/test/Dialect/XeGPU/XeGPUOps.mlir         | 62 ++++++++++++++++++-
 mlir/test/Dialect/XeGPU/invalid.mlir          | 58 +++++++++++++++++
 4 files changed, 154 insertions(+), 10 deletions(-)

diff --git a/mlir/include/mlir/Dialect/XeGPU/IR/XeGPUOps.td b/mlir/include/mlir/Dialect/XeGPU/IR/XeGPUOps.td
index a2bfa721f2515b..c2335eecc3781d 100644
--- a/mlir/include/mlir/Dialect/XeGPU/IR/XeGPUOps.td
+++ b/mlir/include/mlir/Dialect/XeGPU/IR/XeGPUOps.td
@@ -548,9 +548,7 @@ def XeGPU_PrefetchOp : XeGPU_Op<"prefetch", []> {
   let hasVerifier = 1;
 }
 
-def XeGPU_LoadGatherOp : XeGPU_Op<"load", [AllRanksMatch<["value", "TensorDesc"]>,
-                                    AllElementTypesMatch<["value", "TensorDesc"]>,
-                                   AllElementCountsMatch<["value", "TensorDesc"]>]> {
+def XeGPU_LoadGatherOp : XeGPU_Op<"load", [AllElementTypesMatch<["value", "TensorDesc"]>]> {
   let summary = "load a set of scattered data points from memory.";
 
   let description = [{ It (aka. load) load data per each work-item. The output
@@ -620,8 +618,7 @@ def XeGPU_LoadGatherOp : XeGPU_Op<"load", [AllRanksMatch<["value", "TensorDesc"]
   let hasVerifier = 1;
 }
 
-def XeGPU_StoreScatterOp : XeGPU_Op<"store", [AllElementCountsMatch<["value", "TensorDesc"]>,
-                                              AllElementTypesMatch<["value", "TensorDesc"]>]> {
+def XeGPU_StoreScatterOp : XeGPU_Op<"store", [AllElementTypesMatch<["value", "TensorDesc"]>]> {
   let summary = "store data to scattered memory locations.";
   let description = [{ It (aka. store) stores data to scattered memory locations. The value is
   typically a 1D vector. But when the chunk size of the TensorDesc is larger than 1, it will be
diff --git a/mlir/lib/Dialect/XeGPU/IR/XeGPUOps.cpp b/mlir/lib/Dialect/XeGPU/IR/XeGPUOps.cpp
index 15c435f1fa257b..d44d7a1a4561ad 100644
--- a/mlir/lib/Dialect/XeGPU/IR/XeGPUOps.cpp
+++ b/mlir/lib/Dialect/XeGPU/IR/XeGPUOps.cpp
@@ -453,7 +453,16 @@ LogicalResult CreateDescOp::verify() {
   if (shape != tdescShape)
     return emitOpError("Incorrect TensorDesc shape. ")
            << "Expected is " << makeString(shape) << "\n";
-
+  if (auto sgMap = tdescTy.getSGMapAttr()) {
+    if (sgMap.getWiData()[0] > 1)
+      return emitOpError("TensorDesc cannot have wi_data[0] > 1.");
+    if (chunkSize != static_cast<int>(sgMap.getWiData()[1]))
+      return emitOpError("TensorDesc's chunkSize must match wi_data[1].");
+    if (int rank = tdescTy.getRank(); (sgMap.getWiLayout()[2 - rank] == 1))
+      return emitOpError("TensorDesc of a " + std::to_string(rank) +
+                         "D tensor must have wi_layout[" +
+                         std::to_string(2 - rank) + "] == tdescShape[0].");
+  }
   return success();
 }
 
@@ -512,10 +521,21 @@ LogicalResult LoadGatherOp::verify() {
 
   if (tdescTy.getRank() == 2) {
     if (!getTransposeAttr())
-      return emitOpError("load_gather has to be transposed.");
+      return emitOpError("load of rank-2 tensor has to be transposed.");
     transpose({1, 0}, tdescShape);
   }
 
+  if (auto sgMap = tdescTy.getSGMapAttr()) {
+    auto valueVecTy = cast<VectorType>(valueTy);
+    const int32_t wiData =
+        sgMap.getWiData()[0] > 1 ? sgMap.getWiData()[0] : sgMap.getWiData()[1];
+    if (valueVecTy.getNumElements() != wiData ||
+        valueVecTy.getNumElements() != tdescTy.getChunkSize()) {
+      return emitOpError("Chunk size, vector size and wi_data must match.");
+    }
+    tdescShape[tdescTy.getRank() - 1] = 1;
+  }
+
   if (valueShape != tdescShape)
     return emitOpError("Unexpected result shape")
            << "(Expected shape: " << makeString(tdescShape)
@@ -551,10 +571,21 @@ LogicalResult StoreScatterOp::verify() {
 
   if (tdescTy.getRank() == 2) {
     if (!getTransposeAttr())
-      return emitOpError("load_gather has to be transposed.");
+      return emitOpError("Store of a rank-2 tensor has to be transposed.");
     transpose({1, 0}, tdescShape);
   }
 
+  if (auto sgMap = tdescTy.getSGMapAttr()) {
+    auto valueVecTy = cast<VectorType>(valueTy);
+    const int32_t wiData =
+        sgMap.getWiData()[0] > 1 ? sgMap.getWiData()[0] : sgMap.getWiData()[1];
+    if (valueVecTy.getNumElements() != wiData ||
+        valueVecTy.getNumElements() != tdescTy.getChunkSize()) {
+      return emitOpError("Chunk size, vector size and wi_data must match.");
+    }
+    tdescShape[tdescTy.getRank() - 1] = 1;
+  }
+
   if (valueShape != tdescShape)
     return emitOpError("Unexpected value shape")
            << "(Expected shape: " << makeString(tdescShape)
diff --git a/mlir/test/Dialect/XeGPU/XeGPUOps.mlir b/mlir/test/Dialect/XeGPU/XeGPUOps.mlir
index d7174a489888a4..dcd6b01974cf30 100644
--- a/mlir/test/Dialect/XeGPU/XeGPUOps.mlir
+++ b/mlir/test/Dialect/XeGPU/XeGPUOps.mlir
@@ -163,11 +163,69 @@ gpu.func @test_create_tdesc_vc_1(%src: memref<?xf32, 3>) {
 gpu.func @test_create_tdesc_vc_with_sg_map(%src: ui64) {
   //CHECK: %[[cst:.*]] = arith.constant dense<[0, 8, 16, 24]> : vector<4xindex>
   %0 = arith.constant dense<[0, 8, 16, 24]> : vector<4xindex>
-  //CHECK: %[[R0:.*]] = xegpu.create_tdesc %[[arg0]], %[[cst]] : ui64, vector<4xindex> -> !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2 : i64>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 1]>>
-  %1 = xegpu.create_tdesc %src, %0 : ui64, vector<4xindex> -> !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 1]>>
+  //CHECK: %[[R0:.*]] = xegpu.create_tdesc %[[arg0]], %[[cst]] : ui64, vector<4xindex> -> !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2 : i64>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 2]>>
+  %1 = xegpu.create_tdesc %src, %0 : ui64, vector<4xindex> -> !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 2]>>
   gpu.return
 }
 
+// CHECK: gpu.func @test_load_with_sg_map(%[[arg0:.*]]: ui64) {
+gpu.func @test_load_with_sg_map(%src: ui64) {
+  //CHECK: %[[cst:.*]] = arith.constant dense<[0, 8, 16, 24]> : vector<4xindex>
+  %0 = arith.constant dense<[0, 8, 16, 24]> : vector<4xindex>
+  //CHECK: %[[cst1:.*]] = arith.constant dense<true> : vector<4xi1>
+  %1 = arith.constant dense<1>: vector<4xi1>
+  //CHECK: %[[R0:.*]] = xegpu.create_tdesc %[[arg0]], %[[cst]] : ui64, vector<4xindex> -> !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2 : i64>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 2]>>
+  %2 = xegpu.create_tdesc %src, %0 : ui64, vector<4xindex> -> !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 2]>>
+  //CHECK: %[[R1:.*]] = xegpu.load %[[R0]], %[[cst1]] <{l1_hint = #xegpu.cache_hint<cached>, l2_hint = #xegpu.cache_hint<uncached>, transpose}> : !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2 : i64>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 2]>>, vector<4xi1> -> vector<2x1xf32> 
+  %3 = xegpu.load %2, %1 <{l1_hint = #xegpu.cache_hint<cached>, l2_hint = #xegpu.cache_hint<uncached>, transpose}> : !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 2]>>, vector<4xi1> -> vector<2x1xf32>
+  gpu.return
+}
+
+// CHECK: gpu.func @test_load_with_sg_map_2(%[[arg0:.*]]: ui64) {
+gpu.func @test_load_with_sg_map_2(%src: ui64) {
+  //CHECK: %[[cst:.*]] = arith.constant dense<[0, 8, 16, 24]> : vector<4xindex>
+  %0 = arith.constant dense<[0, 8, 16, 24]> : vector<4xindex>
+  //CHECK: %[[cst1:.*]] = arith.constant dense<true> : vector<4xi1>
+  %1 = arith.constant dense<1>: vector<4xi1>
+  //CHECK: %[[R0:.*]] = xegpu.create_tdesc %[[arg0]], %[[cst]] : ui64, vector<4xindex> -> !xegpu.tensor_desc<4xf32, #xegpu.scatter_tdesc_attr<>, #xegpu.sg_map<wi_layout = [1, 4], wi_data = [1, 1]>>
+  %2 = xegpu.create_tdesc %src, %0 : ui64, vector<4xindex> -> !xegpu.tensor_desc<4xf32, #xegpu.scatter_tdesc_attr<>, #xegpu.sg_map<wi_layout = [1, 4], wi_data = [1, 1]>>
+  //CHECK: %[[R1:.*]] = xegpu.load %[[R0]], %[[cst1]] <{l1_hint = #xegpu.cache_hint<cached>, l2_hint = #xegpu.cache_hint<uncached>}> : !xegpu.tensor_desc<4xf32, #xegpu.scatter_tdesc_attr<>, #xegpu.sg_map<wi_layout = [1, 4], wi_data = [1, 1]>>, vector<4xi1> -> vector<1xf32> 
+  %3 = xegpu.load %2, %1 <{l1_hint = #xegpu.cache_hint<cached>, l2_hint = #xegpu.cache_hint<uncached>}> : !xegpu.tensor_desc<4xf32, #xegpu.scatter_tdesc_attr<>, #xegpu.sg_map<wi_layout = [1, 4], wi_data = [1, 1]>>, vector<4xi1> -> vector<1xf32>
+  gpu.return
+}
+
+// CHECK: gpu.func @test_store_with_sg_map(%[[arg0:.*]]: ui64) {
+gpu.func @test_store_with_sg_map(%src: ui64) {
+  //CHECK: %[[cst:.*]] = arith.constant dense<[0, 8, 16, 24]> : vector<4xindex>
+  %0 = arith.constant dense<[0, 8, 16, 24]> : vector<4xindex>
+  //CHECK: %[[cst1:.*]] = arith.constant dense<true> : vector<4xi1>
+  %1 = arith.constant dense<1>: vector<4xi1>
+  //CHECK: %[[cst2:.*]] = arith.constant dense<2.900000e+00> : vector<2x1xf32>
+  %2 = arith.constant dense<2.9>: vector<2x1xf32>
+  //CHECK: %[[R0:.*]] = xegpu.create_tdesc %[[arg0]], %[[cst]] : ui64, vector<4xindex> -> !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2 : i64>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 2]>> 
+  %3 = xegpu.create_tdesc %src, %0 : ui64, vector<4xindex> -> !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 2]>>
+  //CHECK: xegpu.store %[[cst2]], %[[R0]], %[[cst1]] <{l1_hint = #xegpu.cache_hint<write_back>, l2_hint = #xegpu.cache_hint<uncached>, transpose}> : vector<2x1xf32>, !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2 : i64>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 2]>>, vector<4xi1>
+  xegpu.store %2, %3, %1 <{l1_hint = #xegpu.cache_hint<write_back>, l2_hint = #xegpu.cache_hint<uncached>, transpose}> : vector<2x1xf32>, !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 2]>>, vector<4xi1>
+  gpu.return
+}
+
+// CHECK: gpu.func @test_store_with_sg_map_2(%[[arg0:.*]]: ui64) {
+gpu.func @test_store_with_sg_map_2(%src: ui64) {
+  //CHECK: %[[cst:.*]] = arith.constant dense<[0, 8, 16, 24]> : vector<4xindex>
+  %0 = arith.constant dense<[0, 8, 16, 24]> : vector<4xindex>
+  //CHECK: %[[cst1:.*]] = arith.constant dense<true> : vector<4xi1>
+  %1 = arith.constant dense<1>: vector<4xi1>
+  //CHECK: %[[cst2:.*]] = arith.constant dense<2.900000e+00> : vector<1xf32>
+  %2 = arith.constant dense<2.9>: vector<1xf32>
+  //CHECK: %[[R0:.*]] = xegpu.create_tdesc %[[arg0]], %[[cst]] : ui64, vector<4xindex> -> !xegpu.tensor_desc<4xf32, #xegpu.scatter_tdesc_attr<>, #xegpu.sg_map<wi_layout = [1, 4], wi_data = [1, 1]>> 
+  %3 = xegpu.create_tdesc %src, %0 : ui64, vector<4xindex> -> !xegpu.tensor_desc<4xf32, #xegpu.scatter_tdesc_attr<>, #xegpu.sg_map<wi_layout = [1, 4], wi_data = [1, 1]>>
+  //CHECK: xegpu.store %[[cst2]], %[[R0]], %[[cst1]] <{l1_hint = #xegpu.cache_hint<write_back>, l2_hint = #xegpu.cache_hint<uncached>}> : vector<1xf32>, !xegpu.tensor_desc<4xf32, #xegpu.scatter_tdesc_attr<>, #xegpu.sg_map<wi_layout = [1, 4], wi_data = [1, 1]>>, vector<4xi1>
+  xegpu.store %2, %3, %1 <{l1_hint = #xegpu.cache_hint<write_back>, l2_hint = #xegpu.cache_hint<uncached>}> : vector<1xf32>, !xegpu.tensor_desc<4xf32, #xegpu.scatter_tdesc_attr<>, #xegpu.sg_map<wi_layout = [1, 4], wi_data = [1, 1]>>, vector<4xi1>
+  gpu.return
+}
+
+
+
 // CHECK: gpu.func @test_prefetch_vc(%[[arg0:.*]]: ui64) {
 gpu.func @test_prefetch_vc(%src: ui64) {
   //CHECK: %[[cst:.*]] = arith.constant dense<[0, 8, 16, 24]> : vector<4xindex>
diff --git a/mlir/test/Dialect/XeGPU/invalid.mlir b/mlir/test/Dialect/XeGPU/invalid.mlir
index 7816bff0582f81..26cc2a7848d723 100644
--- a/mlir/test/Dialect/XeGPU/invalid.mlir
+++ b/mlir/test/Dialect/XeGPU/invalid.mlir
@@ -170,6 +170,64 @@ func.func @test_prefetch_vc_2(%src: ui64) {
   return
 }
 
+// -----
+func.func @test_create_tdesc_sg_map_1(%src: ui64) {
+  %cst = arith.constant dense<[0, 8, 16, 24]> : vector<4xindex>
+  // expected-error at +1 {{TensorDesc of a 1D tensor must have wi_layout[1] == tdescShape[0]}}
+  %1 = xegpu.create_tdesc %src, %cst : ui64, vector<4xindex> -> !xegpu.tensor_desc<4xf32, #xegpu.scatter_tdesc_attr<>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 1]>>
+  return
+}
+
+// -----
+func.func @test_create_tdesc_sg_map_2(%src: ui64) {
+  %cst = arith.constant dense<[0, 8, 16, 24]> : vector<4xindex>
+  // expected-error at +1 {{TensorDesc cannot have wi_data[0] > 1}}
+  %1 = xegpu.create_tdesc %src, %cst : ui64, vector<4xindex> -> !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2>, #xegpu.sg_map<wi_layout = [1, 4], wi_data = [2, 1]>>
+  return
+}
+
+// -----
+func.func @test_load_gather_sg_map_1(%src: ui64) {
+  %0 = arith.constant dense<1>: vector<4xi1>
+  %cst = arith.constant dense<[0, 8, 16, 24]> : vector<4xindex>
+  %1 = xegpu.create_tdesc %src, %cst : ui64, vector<4xindex> -> !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 2]>>
+  // expected-error at +1 {{Unexpected result shape(Expected shape: [2, 1], Given shape: [1, 2])}}
+  %2 = xegpu.load %1, %0 <{l1_hint = #xegpu.cache_hint<cached>, transpose}> : !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 2]>>, vector<4xi1> -> vector<1x2xf32>
+  return
+}
+
+// -----
+func.func @test_load_gather_sg_map_2(%src: ui64) {
+  %0 = arith.constant dense<1>: vector<4xi1>
+  %cst = arith.constant dense<[0, 8, 16, 24]> : vector<4xindex>
+  %1 = xegpu.create_tdesc %src, %cst : ui64, vector<4xindex> -> !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 2]>>
+  // expected-error at +1 {{Unexpected result shape(Expected shape: [2, 1], Given shape: [2])}}
+  %2 = xegpu.load %1, %0 <{l1_hint = #xegpu.cache_hint<cached>, transpose}> : !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 2]>>, vector<4xi1> -> vector<2xf32>
+  return
+}
+
+// -----
+func.func @test_store_scatter_sg_map_1(%src: ui64) {
+  %0 = arith.constant dense<1>: vector<4xi1>
+  %cst = arith.constant dense<[0, 8, 16, 24]> : vector<4xindex>
+  %val = arith.constant dense<2.9>: vector<1x2xf32>
+  %1 = xegpu.create_tdesc %src, %cst : ui64, vector<4xindex> -> !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 2]>>
+  // expected-error at +1 {{Unexpected value shape(Expected shape: [2, 1], Given shape: [1, 2])}}
+  xegpu.store %val, %1, %0 <{l1_hint = #xegpu.cache_hint<cached>, transpose}> : vector<1x2xf32>, !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 2]>>, vector<4xi1>
+  return
+}
+
+// -----
+func.func @test_store_scatter_sg_map_2(%src: ui64) {
+  %0 = arith.constant dense<1>: vector<4xi1>
+  %cst = arith.constant dense<[0, 8, 16, 24]> : vector<4xindex>
+  %val = arith.constant dense<2.9>: vector<2xf32>
+  %1 = xegpu.create_tdesc %src, %cst : ui64, vector<4xindex> -> !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 2]>>
+  // expected-error at +1 {{Unexpected value shape(Expected shape: [2, 1], Given shape: [2])}}
+  xegpu.store %val, %1, %0 <{l1_hint = #xegpu.cache_hint<cached>, transpose}> : vector<2xf32>, !xegpu.tensor_desc<4x2xf32, #xegpu.scatter_tdesc_attr<chunk_size = 2>, #xegpu.sg_map<wi_layout = [4, 1], wi_data = [1, 2]>>, vector<4xi1>
+  return
+}
+
 // -----
 func.func @test_load_gather_vc_1(%src: memref<24x32xf16>) {
   %0 = arith.constant dense<1>: vector<4xi1>



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