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[WIP][DO NOT REVIEW] i8->i8 Phoenix|Strix + Ukernel|Vectorization #1084

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[WIP][DO NOT REVIEW] i8->i8 Phoenix|Strix + Ukernel|Vectorization #1084

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e3f386d
WIP i8->i8 Strix
Abhishek-Varma Feb 4, 2025
bcd5f08
Phoenix ukernel + vectorization support
Abhishek-Varma Feb 6, 2025
6406980
Revert previous tests
Abhishek-Varma Feb 6, 2025
99cbf3f
Add insert-cores lit test
Abhishek-Varma Feb 6, 2025
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13 changes: 13 additions & 0 deletions build_tools/ci/cpu_comparison/matmul_template/matmul_trunci_MxK_KxN.mlir
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Original file line number Diff line number Diff line change
@@ -0,0 +1,13 @@
// input ${M}x${K}x${TYPE1}
// input ${K}x${N}x${TYPE1}

func.func @matmul_trunci(%arg0: tensor<${M}x${K}x${TYPE1}>, %arg1: tensor<${K}x${N}x${TYPE1}>) -> tensor<${M}x${N}x${TYPE1}>
{
%cst = arith.constant ${ZERO} : ${TYPE2}
%0 = tensor.empty() : tensor<${M}x${N}x${TYPE2}>
%1 = linalg.fill ins(%cst : ${TYPE2}) outs(%0 : tensor<${M}x${N}x${TYPE2}>) -> tensor<${M}x${N}x${TYPE2}>
%2 = linalg.matmul ins(%arg0, %arg1 : tensor<${M}x${K}x${TYPE1}>, tensor<${K}x${N}x${TYPE1}>)
outs(%1: tensor<${M}x${N}x${TYPE2}>) -> tensor<${M}x${N}x${TYPE2}>
%3 = arith.trunci %2 : tensor<${M}x${N}x${TYPE2}> to tensor<${M}x${N}x${TYPE1}>
return %3: tensor<${M}x${N}x${TYPE1}>
}
143 changes: 142 additions & 1 deletion build_tools/ci/cpu_comparison/run.py
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Original file line number Diff line number Diff line change
Expand Up @@ -634,10 +634,11 @@ def __init__(
expected_out,
run_on_target=["npu1_4col"],
tile_pipeline="pack-peel",
aie_compilation_flags=None,
):
super().__init__(
run_on_target=run_on_target,
aie_compilation_flags=None,
aie_compilation_flags=aie_compilation_flags,
M=M,
N=M,
K=K,
Expand Down Expand Up @@ -694,6 +695,88 @@ def _execute(self, config):
return True


class MatmulTrunci(BaseMatmul):
"""
A test of the form matmul(A,B) + trunci(C) where A:MxK, B:KxM and C:MxM
"""

def __init__(
self,
M,
K,
input_type,
acc_type,
lhs,
rhs,
expected_out,
run_on_target=["npu1_4col"],
tile_pipeline="pack-peel",
aie_compilation_flags=None,
use_ukernel=False,
n_repeats=1,
use_chess=False,
):
super().__init__(
run_on_target=run_on_target,
aie_compilation_flags=aie_compilation_flags,
M=M,
N=M,
K=K,
input_type=input_type,
acc_type=acc_type,
tile_pipeline=tile_pipeline,
n_repeats=n_repeats,
use_ukernel=use_ukernel,
use_chess=use_chess,
)
self.labels.append("MatmulTrunci")

# Assertions on shapes: Check that lhs is MxK, rhs is KxM, and expected_out is MxM
assert lhs.shape == (M, K)
assert rhs.shape == (K, M)
assert expected_out.shape == (M, M)

self.name = f"matmul_trunci_{M}_{K}_{input_type}_{acc_type}"
if tile_pipeline == "pack-peel-4-level-tiling":
self.name += "_4_level_tiling"
self.lhs = lhs
self.rhs = rhs
self.expected_out = expected_out

def _execute(self, config):
matmul_template_dir = config.file_dir / "matmul_template"
template_name = matmul_template_dir / "matmul_trunci_MxK_KxN.mlir"
self.generate(config, template_name)
filename = self.get_filename(config)
input_args = generate_inputs(
filename, self.get_dir(config), 1, {1: self.lhs, 2: self.rhs}
)
"""
Currently without function outlining, we run out of program memory.
"""
self.add_aie_compilation_flags(
["--iree-amdaie-enable-function-outlining=balanced"]
)
aie_vs_baseline(
config=config,
aie_compilation_flags=self.aie_compilation_flags,
test_file=self.get_filename(config),
input_args=input_args,
baseline_value=self.expected_out,
use_ukernel=self.use_ukernel,
tile_pipeline=self.tile_pipeline,
function_name=None,
seed=1,
rtol=0,
atol=0,
lower_to_aie_pipeline=self.lower_to_aie_pipeline,
n_repeats=self.n_repeats,
output_type=get_output_type(self.get_filename(config)),
)

return True


def find_executable(install_dir: Path, executable_name):
"""
Search for an executable in the given directory and its subdirectories
Expand Down Expand Up @@ -1475,6 +1558,64 @@ def __init__(self):
self.existing_names = []
self.tests = []

# Tests Matmul + Trunci.
# Phoenix : Ukernel + Peano.
self.register(
MatmulTrunci(
256,
32,
"i8",
"i32",
1 * np.ones([256, 32], dtype=np.int8),
1 * np.ones([32, 256], dtype=np.int8),
32 * np.ones([256, 256], dtype=np.int8),
tile_pipeline="pack-peel-4-level-tiling",
run_on_target=["npu1_4col"],
aie_compilation_flags=[
"--iree-amdaie-num-rows=4",
"--iree-amdaie-num-cols=4",
],
use_ukernel=True,
)
)
# Phoenix : Vectorization + Peano.
self.register(
MatmulTrunci(
256,
32,
"i8",
"i32",
1 * np.ones([256, 32], dtype=np.int8),
1 * np.ones([32, 256], dtype=np.int8),
32 * np.ones([256, 256], dtype=np.int8),
tile_pipeline="pack-peel-4-level-tiling",
run_on_target=["npu1_4col"],
aie_compilation_flags=[
"--iree-amdaie-num-rows=4",
"--iree-amdaie-num-cols=4",
],
)
)
# Strix : Ukernel + Chess.
self.register(
MatmulTrunci(
256,
32,
"i8",
"i32",
1 * np.ones([256, 32], dtype=np.int8),
1 * np.ones([32, 256], dtype=np.int8),
32 * np.ones([256, 256], dtype=np.int8),
tile_pipeline="pack-peel-4-level-tiling",
run_on_target=["npu4"],
aie_compilation_flags=[
"--iree-amdaie-num-rows=4",
"--iree-amdaie-num-cols=8",
],
use_chess=True,
use_ukernel=True,
)
)
# Matmul with truncf test(s):
for tile_pipeline in ["pack-peel", "pack-peel-4-level-tiling"]:
self.register(
Expand Down
20 changes: 11 additions & 9 deletions compiler/plugins/target/AMD-AIE/aievec/VectorToVectorConversions.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -802,10 +802,11 @@ struct ToMinorIdentityTransferReadPattern
/// %1 = arith.truncf %0 : vector<6xf32> to vector<6xbf16>
/// %2 = vector.shape_cast %1 : vector<6xbf16> to vector<2x3xbf16>
// clang-format on
struct FlattenArithTruncFOpPattern : public OpRewritePattern<arith::TruncFOp> {
using OpRewritePattern<arith::TruncFOp>::OpRewritePattern;
template <typename TruncOpTy>
struct FlattenArithTruncOpPattern : public OpRewritePattern<TruncOpTy> {
using OpRewritePattern<TruncOpTy>::OpRewritePattern;

LogicalResult matchAndRewrite(arith::TruncFOp op,
LogicalResult matchAndRewrite(TruncOpTy op,
PatternRewriter &rewriter) const override {
// Get old shape type.
auto oldShapedType = dyn_cast<VectorType>(op.getType());
Expand All @@ -826,7 +827,7 @@ struct FlattenArithTruncFOpPattern : public OpRewritePattern<arith::TruncFOp> {
Value newInputVector = rewriter.create<vector::ShapeCastOp>(
op.getLoc(), newVectorTypeForInput, origInputOfTruncFOp);
// Create new base operation with the linearized input/output.
Value newTruncFOp = rewriter.create<arith::TruncFOp>(
Value newTruncFOp = rewriter.create<TruncOpTy>(
op.getLoc(), newVectorTypeForOutput, newInputVector);
// Delinearize the output back to the original type.
rewriter.replaceOpWithNewOp<vector::ShapeCastOp>(op, op.getType(),
Expand Down Expand Up @@ -1054,11 +1055,12 @@ struct CanonicalizeVectorForAIEVecPass

{
RewritePatternSet patterns(context);
patterns
.add<ExtractTransposeFromContractionOp, FlattenArithTruncFOpPattern,
ToMinorIdentityTransferReadPattern,
ToMinorIdentityTransferWritePattern,
ConvertLeadingUnitDimInsertToReshapePattern>(context);
patterns.add<ExtractTransposeFromContractionOp,
FlattenArithTruncOpPattern<arith::TruncFOp>,
FlattenArithTruncOpPattern<arith::TruncIOp>,
ToMinorIdentityTransferReadPattern,
ToMinorIdentityTransferWritePattern,
ConvertLeadingUnitDimInsertToReshapePattern>(context);
patterns.add<ConvertSplatTransferReadToBroadcastPattern>(context);
patterns
.add<copied_from_mlir::FlattenContiguousRowMajorTransferReadPattern,
Expand Down
13 changes: 13 additions & 0 deletions compiler/plugins/target/AMD-AIE/aievec/test/precanonicalization-aieml-llvmir.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -169,6 +169,19 @@ func.func @arith_truncf(%inp: vector<2x3xf32>) -> vector<2x3xbf16> {

// -----

// CHECK-LABEL: @arith_trunci(
// CHECK-SAME: %[[INP:.*]]: vector<2x3xi32>)
func.func @arith_trunci(%inp: vector<2x3xi32>) -> vector<2x3xi8> {
// CHECK: %[[LINEARIZE:.*]] = vector.shape_cast %[[INP]] : vector<2x3xi32> to vector<6xi32>
// CHECK: %[[TRUNCI:.*]] = arith.trunci %[[LINEARIZE]] : vector<6xi32> to vector<6xi8>
// CHECK: %[[DELINEARIZE:.*]] = vector.shape_cast %[[TRUNCI]] : vector<6xi8> to vector<2x3xi8>
// CHECK: return %[[DELINEARIZE]]
%0 = arith.trunci %inp : vector<2x3xi32> to vector<2x3xi8>
return %0 : vector<2x3xi8>
}

// -----

// CHECK: #map = affine_map<()[s0] -> (s0 * 256 + 96)>
// CHECK-LABEL: @trivial_read_access
// CHECK-SAME: (%[[ARG0:.*]]: memref<4x8x4x8xbf16, strided<[256, 32, 8, 1]>>,
Expand Down
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