1 //===------ WmmaOpsToNVVM.cpp - WMMA LD/ST/Compute to NVVM lowering -------===//
2 //
3 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
4 // See https://llvm.org/LICENSE.txt for license information.
5 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
6 //
7 //===----------------------------------------------------------------------===//
8 //
9 // This file contains definitions of patterns to lower GPU Subgroup MMA ops to
10 // NVVM Dialect.
11 //
12 //===----------------------------------------------------------------------===//
13
14 #include "mlir/Conversion/GPUToNVVM/GPUToNVVMPass.h"
15 #include "mlir/Conversion/LLVMCommon/Pattern.h"
16 #include "mlir/Dialect/GPU/IR/GPUDialect.h"
17 #include "mlir/Dialect/LLVMIR/LLVMDialect.h"
18 #include "mlir/Dialect/LLVMIR/NVVMDialect.h"
19 #include "mlir/IR/TypeUtilities.h"
20
21 using namespace mlir;
22
23 namespace {
24
25 /// Checks if all the operands of the op being lowered are of LLVM Types. The
26 /// types are expected to be converted by the `LLVMTypeConverter` before the op
27 /// is actually lowered. If the type of an operands is not already converted it
28 /// hints a missing typeConversion and failure is returned in that case.
areAllLLVMTypes(Operation * op,ValueRange operands,ConversionPatternRewriter & rewriter)29 static LogicalResult areAllLLVMTypes(Operation *op, ValueRange operands,
30 ConversionPatternRewriter &rewriter) {
31 if (!llvm::all_of(operands, [](Value value) {
32 return LLVM::isCompatibleType(value.getType());
33 })) {
34 return rewriter.notifyMatchFailure(
35 op, "cannot convert if operands aren't of LLVM type.");
36 }
37
38 return success();
39 }
40
41 /// Error string to emit when an unimplemented WMMA variant is encountered.
42 static constexpr StringRef kInvalidCaseStr = "Unsupported WMMA variant.";
43
convertOperand(StringRef operandName)44 static NVVM::MMAFrag convertOperand(StringRef operandName) {
45 if (operandName.equals("AOp"))
46 return NVVM::MMAFrag::a;
47 if (operandName.equals("BOp"))
48 return NVVM::MMAFrag::b;
49 if (operandName.equals("COp"))
50 return NVVM::MMAFrag::c;
51 llvm_unreachable("Unknown operand name");
52 }
53
getElementType(gpu::MMAMatrixType type)54 static NVVM::MMATypes getElementType(gpu::MMAMatrixType type) {
55 if (type.getElementType().isF16())
56 return NVVM::MMATypes::f16;
57 if (type.getElementType().isF32())
58 return type.getOperand().equals("COp") ? NVVM::MMATypes::f32
59 : NVVM::MMATypes::tf32;
60 llvm_unreachable("Unsupported type");
61 }
62
63 /// This class implements the conversion of GPU MMA loadOp to wmma.load op
64 /// in the NVVM dialect. The conversion not only emits the NVVM op but also
65 /// emits code that is necessary to store the data in the destination memref
66 /// after it has been loaded.
67 struct WmmaLoadOpToNVVMLowering
68 : public ConvertOpToLLVMPattern<gpu::SubgroupMmaLoadMatrixOp> {
69 using ConvertOpToLLVMPattern<
70 gpu::SubgroupMmaLoadMatrixOp>::ConvertOpToLLVMPattern;
71
72 LogicalResult
matchAndRewrite__anonc5aca7e10111::WmmaLoadOpToNVVMLowering73 matchAndRewrite(gpu::SubgroupMmaLoadMatrixOp subgroupMmaLoadMatrixOp,
74 OpAdaptor adaptor,
75 ConversionPatternRewriter &rewriter) const override {
76 Operation *op = subgroupMmaLoadMatrixOp.getOperation();
77 if (failed(areAllLLVMTypes(op, adaptor.getOperands(), rewriter)))
78 return failure();
79
80 // Get the shape of the MMAMatrix type being returned. The shape will
81 // choose which intrinsic this op will be lowered to.
82 gpu::MMAMatrixType retType =
83 subgroupMmaLoadMatrixOp.res().getType().cast<gpu::MMAMatrixType>();
84 ArrayRef<int64_t> retTypeShape = retType.getShape();
85 int64_t m = 0;
86 int64_t n = 0;
87 int64_t k = 0;
88 NVVM::MMATypes eltype = getElementType(retType);
89 // NVVM intrinsics require to give mxnxk dimensions, infer the missing
90 // dimension based on the valid intrinsics available.
91 if (retType.getOperand().equals("AOp")) {
92 m = retTypeShape[0];
93 k = retTypeShape[1];
94 n = NVVM::WMMALoadOp::inferNDimension(m, k, eltype);
95 } else if (retType.getOperand().equals("BOp")) {
96 k = retTypeShape[0];
97 n = retTypeShape[1];
98 m = NVVM::WMMALoadOp::inferMDimension(k, n, eltype);
99 } else if (retType.getOperand().equals("COp")) {
100 m = retTypeShape[0];
101 n = retTypeShape[1];
102 k = NVVM::WMMALoadOp::inferKDimension(m, n, eltype);
103 }
104 NVVM::MMALayout layout = NVVM::MMALayout::row;
105 NVVM::MMAFrag frag = convertOperand(retType.getOperand());
106 // Check that there is an exisiting instruction for the combination we need.
107 if (NVVM::WMMALoadOp::getIntrinsicID(m, n, k, layout, eltype, frag) == 0)
108 return rewriter.notifyMatchFailure(op, kInvalidCaseStr);
109
110 Type resType = convertMMAToLLVMType(retType);
111 Location loc = op->getLoc();
112
113 // Create nvvm.mma_load op according to the operand types.
114 Value dataPtr = getStridedElementPtr(
115 loc, subgroupMmaLoadMatrixOp.srcMemref().getType().cast<MemRefType>(),
116 adaptor.srcMemref(), adaptor.indices(), rewriter);
117
118 Value leadingDim = rewriter.create<LLVM::ConstantOp>(
119 loc, rewriter.getI32Type(),
120 subgroupMmaLoadMatrixOp.leadDimensionAttr());
121 rewriter.replaceOpWithNewOp<NVVM::WMMALoadOp>(
122 op, resType, dataPtr, leadingDim, m, n, k, layout, eltype, frag);
123 return success();
124 }
125 };
126
127 /// This class implements the conversion of GPU MMA storeOp to wmma.store op
128 /// in the NVVM dialect. The conversion not only emits the NVVM op but also
129 /// emits code that is necessary to unpack the data in the source and
130 /// convert the data in the format that is needed by the NVVM op.
131 struct WmmaStoreOpToNVVMLowering
132 : public ConvertOpToLLVMPattern<gpu::SubgroupMmaStoreMatrixOp> {
133 using ConvertOpToLLVMPattern<
134 gpu::SubgroupMmaStoreMatrixOp>::ConvertOpToLLVMPattern;
135
136 LogicalResult
matchAndRewrite__anonc5aca7e10111::WmmaStoreOpToNVVMLowering137 matchAndRewrite(gpu::SubgroupMmaStoreMatrixOp subgroupMmaStoreMatrixOp,
138 OpAdaptor adaptor,
139 ConversionPatternRewriter &rewriter) const override {
140 Operation *op = subgroupMmaStoreMatrixOp.getOperation();
141 if (failed(areAllLLVMTypes(op, adaptor.getOperands(), rewriter)))
142 return failure();
143
144 Location loc = op->getLoc();
145
146 SmallVector<Value, 4> storeOpOperands;
147 // Get the shape of the MMAMatrix type being stored. The shape will
148 // choose which intrinsic this op will be lowered to.
149 gpu::MMAMatrixType srcType =
150 subgroupMmaStoreMatrixOp.src().getType().cast<gpu::MMAMatrixType>();
151 ArrayRef<int64_t> srcTypeShape = srcType.getShape();
152 NVVM::MMALayout layout = NVVM::MMALayout::row;
153 NVVM::MMATypes eltype = getElementType(srcType);
154 int64_t m = srcTypeShape[0];
155 int64_t n = srcTypeShape[1];
156 int64_t k = NVVM::WMMAStoreOp::inferKDimension(m, n, eltype);
157 if (NVVM::WMMAStoreOp::getIntrinsicID(m, n, k, layout, eltype) == 0)
158 return rewriter.notifyMatchFailure(op, kInvalidCaseStr);
159
160 auto matrixType = adaptor.src().getType().cast<LLVM::LLVMStructType>();
161 for (unsigned i = 0, e = matrixType.getBody().size(); i < e; ++i) {
162 Value toUse = rewriter.create<LLVM::ExtractValueOp>(
163 loc, matrixType.getBody()[i], adaptor.src(),
164 rewriter.getI32ArrayAttr(i));
165 storeOpOperands.push_back(toUse);
166 }
167
168 Value dataPtr = getStridedElementPtr(
169 loc, subgroupMmaStoreMatrixOp.dstMemref().getType().cast<MemRefType>(),
170 adaptor.dstMemref(), adaptor.indices(), rewriter);
171 Value leadingDim = rewriter.create<LLVM::ConstantOp>(
172 loc, rewriter.getI32Type(),
173 subgroupMmaStoreMatrixOp.leadDimensionAttr());
174 rewriter.replaceOpWithNewOp<NVVM::WMMAStoreOp>(
175 op, dataPtr, m, n, k, layout, eltype, storeOpOperands, leadingDim);
176 return success();
177 }
178 };
179
180 /// This class implements the conversion of GPU MMA computeOp to wmma.mma op
181 /// in the NVVM dialect.
182 struct WmmaMmaOpToNVVMLowering
183 : public ConvertOpToLLVMPattern<gpu::SubgroupMmaComputeOp> {
184 using ConvertOpToLLVMPattern<
185 gpu::SubgroupMmaComputeOp>::ConvertOpToLLVMPattern;
186
187 LogicalResult
matchAndRewrite__anonc5aca7e10111::WmmaMmaOpToNVVMLowering188 matchAndRewrite(gpu::SubgroupMmaComputeOp subgroupMmaComputeOp,
189 OpAdaptor adaptor,
190 ConversionPatternRewriter &rewriter) const override {
191 Operation *op = subgroupMmaComputeOp.getOperation();
192 if (failed(areAllLLVMTypes(op, adaptor.getOperands(), rewriter)))
193 return failure();
194
195 Location loc = op->getLoc();
196
197 // The wmma.mma intrinsic in llvm requires the operands as individual
198 // values. So individual elements from the memrefs need to be extracted and
199 // then passed on to the intrinsic call. Emit llvm ops to extract individual
200 // values form lowered memrefs.
201 SmallVector<Value> unpackedOps;
202
203 auto unpackOp = [&](Value operand) {
204 auto structType = operand.getType().cast<LLVM::LLVMStructType>();
205 for (size_t i = 0, e = structType.getBody().size(); i < e; ++i) {
206 Value toUse = rewriter.create<LLVM::ExtractValueOp>(
207 loc, structType.getBody()[i], operand, rewriter.getI32ArrayAttr(i));
208 unpackedOps.push_back(toUse);
209 }
210 };
211
212 // Get the shapes of the MMAMatrix type being used. The shapes will
213 // choose which intrinsic this op will be lowered to.
214 gpu::MMAMatrixType aType =
215 subgroupMmaComputeOp.opA().getType().cast<gpu::MMAMatrixType>();
216 ArrayRef<int64_t> aTypeShape = aType.getShape();
217 gpu::MMAMatrixType cType =
218 subgroupMmaComputeOp.opC().getType().cast<gpu::MMAMatrixType>();
219 ArrayRef<int64_t> cTypeShape = cType.getShape();
220 int64_t m = cTypeShape[0];
221 int64_t n = cTypeShape[1];
222 int64_t k = aTypeShape[1];
223 NVVM::MMALayout layout = NVVM::MMALayout::row;
224 NVVM::MMATypes sourceType = getElementType(aType);
225 NVVM::MMATypes destType = getElementType(cType);
226 if (NVVM::WMMAMmaOp::getIntrinsicID(m, n, k, layout, layout, sourceType,
227 destType) == 0)
228 return rewriter.notifyMatchFailure(op, kInvalidCaseStr);
229
230 unpackOp(adaptor.opA());
231 unpackOp(adaptor.opB());
232 unpackOp(adaptor.opC());
233
234 rewriter.replaceOpWithNewOp<NVVM::WMMAMmaOp>(
235 op, adaptor.opC().getType(), m, n, k, layout, layout, sourceType,
236 destType, unpackedOps);
237 return success();
238 }
239 };
240
241 /// Convert GPU MMA ConstantMatrixOp to a chain of InsertValueOp.
242 struct WmmaConstantOpToNVVMLowering
243 : public ConvertOpToLLVMPattern<gpu::SubgroupMmaConstantMatrixOp> {
244 using ConvertOpToLLVMPattern<
245 gpu::SubgroupMmaConstantMatrixOp>::ConvertOpToLLVMPattern;
246
247 LogicalResult
matchAndRewrite__anonc5aca7e10111::WmmaConstantOpToNVVMLowering248 matchAndRewrite(gpu::SubgroupMmaConstantMatrixOp subgroupMmaConstantOp,
249 OpAdaptor adaptor,
250 ConversionPatternRewriter &rewriter) const override {
251 if (failed(areAllLLVMTypes(subgroupMmaConstantOp.getOperation(),
252 adaptor.getOperands(), rewriter)))
253 return failure();
254 Location loc = subgroupMmaConstantOp.getLoc();
255 Value cst = adaptor.getOperands()[0];
256 LLVM::LLVMStructType type = convertMMAToLLVMType(
257 subgroupMmaConstantOp.getType().cast<gpu::MMAMatrixType>());
258 // If the element type is a vector create a vector from the operand.
259 if (auto vecType = type.getBody()[0].dyn_cast<VectorType>()) {
260 Value vecCst = rewriter.create<LLVM::UndefOp>(loc, vecType);
261 for (int64_t vecEl = 0; vecEl < vecType.getNumElements(); vecEl++) {
262 Value idx = rewriter.create<LLVM::ConstantOp>(
263 loc, typeConverter->convertType(rewriter.getIntegerType(32)),
264 rewriter.getI32IntegerAttr(vecEl));
265 vecCst = rewriter.create<LLVM::InsertElementOp>(loc, vecType, vecCst,
266 cst, idx);
267 }
268 cst = vecCst;
269 }
270 Value matrixStruct = rewriter.create<LLVM::UndefOp>(loc, type);
271 for (size_t i : llvm::seq(size_t(0), type.getBody().size())) {
272 matrixStruct = rewriter.create<LLVM::InsertValueOp>(
273 loc, matrixStruct, cst, rewriter.getI32ArrayAttr(i));
274 }
275 rewriter.replaceOp(subgroupMmaConstantOp, matrixStruct);
276 return success();
277 }
278 };
279
createMinMaxF(OpBuilder & builder,Location loc,Value lhs,Value rhs,bool isMin)280 static Value createMinMaxF(OpBuilder &builder, Location loc, Value lhs,
281 Value rhs, bool isMin) {
282 auto floatType = getElementTypeOrSelf(lhs.getType()).cast<FloatType>();
283 Type i1Type = builder.getI1Type();
284 if (auto vecType = lhs.getType().dyn_cast<VectorType>())
285 i1Type = VectorType::get(vecType.getShape(), i1Type);
286 Value cmp = builder.create<LLVM::FCmpOp>(
287 loc, i1Type, isMin ? LLVM::FCmpPredicate::olt : LLVM::FCmpPredicate::ogt,
288 lhs, rhs);
289 Value sel = builder.create<LLVM::SelectOp>(loc, cmp, lhs, rhs);
290 Value isNan = builder.create<LLVM::FCmpOp>(
291 loc, i1Type, LLVM::FCmpPredicate::uno, lhs, rhs);
292 Value nan = builder.create<LLVM::ConstantOp>(
293 loc, lhs.getType(),
294 builder.getFloatAttr(floatType,
295 APFloat::getQNaN(floatType.getFloatSemantics())));
296 return builder.create<LLVM::SelectOp>(loc, isNan, nan, sel);
297 }
298
createScalarOp(OpBuilder & builder,Location loc,gpu::MMAElementwiseOp op,ArrayRef<Value> operands)299 static Value createScalarOp(OpBuilder &builder, Location loc,
300 gpu::MMAElementwiseOp op,
301 ArrayRef<Value> operands) {
302 switch (op) {
303 case gpu::MMAElementwiseOp::ADDF:
304 return builder.create<LLVM::FAddOp>(loc, operands[0].getType(), operands);
305 case gpu::MMAElementwiseOp::MULF:
306 return builder.create<LLVM::FMulOp>(loc, operands[0].getType(), operands);
307 case gpu::MMAElementwiseOp::DIVF:
308 return builder.create<LLVM::FDivOp>(loc, operands[0].getType(), operands);
309 case gpu::MMAElementwiseOp::MAXF:
310 return createMinMaxF(builder, loc, operands[0], operands[1],
311 /*isMin=*/false);
312 case gpu::MMAElementwiseOp::MINF:
313 return createMinMaxF(builder, loc, operands[0], operands[1],
314 /*isMin=*/true);
315 }
316 llvm_unreachable("unknown op");
317 }
318
319 /// Convert GPU MMA elementwise ops to extract + op + insert.
320 struct WmmaElementwiseOpToNVVMLowering
321 : public ConvertOpToLLVMPattern<gpu::SubgroupMmaElementwiseOp> {
322 using ConvertOpToLLVMPattern<
323 gpu::SubgroupMmaElementwiseOp>::ConvertOpToLLVMPattern;
324
325 LogicalResult
matchAndRewrite__anonc5aca7e10111::WmmaElementwiseOpToNVVMLowering326 matchAndRewrite(gpu::SubgroupMmaElementwiseOp subgroupMmaElementwiseOp,
327 OpAdaptor adaptor,
328 ConversionPatternRewriter &rewriter) const override {
329 if (failed(areAllLLVMTypes(subgroupMmaElementwiseOp.getOperation(),
330 adaptor.getOperands(), rewriter)))
331 return failure();
332 Location loc = subgroupMmaElementwiseOp.getLoc();
333 size_t numOperands = adaptor.getOperands().size();
334 LLVM::LLVMStructType destType = convertMMAToLLVMType(
335 subgroupMmaElementwiseOp.getType().cast<gpu::MMAMatrixType>());
336 Value matrixStruct = rewriter.create<LLVM::UndefOp>(loc, destType);
337 for (size_t i = 0, e = destType.getBody().size(); i < e; ++i) {
338 SmallVector<Value> extractedOperands;
339 for (size_t opIdx = 0; opIdx < numOperands; opIdx++) {
340 Type elementType = adaptor.getOperands()[opIdx]
341 .getType()
342 .cast<LLVM::LLVMStructType>()
343 .getBody()[i];
344 extractedOperands.push_back(rewriter.create<LLVM::ExtractValueOp>(
345 loc, elementType, adaptor.getOperands()[opIdx],
346 rewriter.getI32ArrayAttr(i)));
347 }
348 Value element =
349 createScalarOp(rewriter, loc, subgroupMmaElementwiseOp.operation(),
350 extractedOperands);
351 matrixStruct = rewriter.create<LLVM::InsertValueOp>(
352 loc, matrixStruct, element, rewriter.getI32ArrayAttr(i));
353 }
354 rewriter.replaceOp(subgroupMmaElementwiseOp, matrixStruct);
355 return success();
356 }
357 };
358
359 } // namespace
360
361 /// Return the LLVMStructureType corresponding to the MMAMatrixType `type`.
convertMMAToLLVMType(gpu::MMAMatrixType type)362 LLVM::LLVMStructType mlir::convertMMAToLLVMType(gpu::MMAMatrixType type) {
363 NVVM::MMAFrag frag = convertOperand(type.getOperand());
364 NVVM::MMATypes eltType = getElementType(type);
365 std::pair<Type, unsigned> typeInfo =
366 NVVM::inferMMAType(eltType, frag, type.getContext());
367 return LLVM::LLVMStructType::getLiteral(
368 type.getContext(), SmallVector<Type, 8>(typeInfo.second, typeInfo.first));
369 }
370
populateGpuWMMAToNVVMConversionPatterns(LLVMTypeConverter & converter,RewritePatternSet & patterns)371 void mlir::populateGpuWMMAToNVVMConversionPatterns(
372 LLVMTypeConverter &converter, RewritePatternSet &patterns) {
373 patterns.add<WmmaLoadOpToNVVMLowering, WmmaMmaOpToNVVMLowering,
374 WmmaStoreOpToNVVMLowering, WmmaConstantOpToNVVMLowering,
375 WmmaElementwiseOpToNVVMLowering>(converter);
376 }
377