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Integrate LLVM at 6d38dbf6eb56fd2b3399565af455de96a99ffa0f #4103

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Integrate LLVM at 6d38dbf6eb56fd2b3399565af455de96a99ffa0f #4103

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52f8bff
[TOSA] TOSA updates for LLVM hash 6d38dbf
justin-ngo-arm Mar 12, 2025
4956e73
Integrate LLVM at 6d38dbf6eb56fd2b3399565af455de96a99ffa0f
vivekkhandelwal1 Mar 24, 2025
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2 changes: 1 addition & 1 deletion externals/llvm-project
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Submodule llvm-project updated 4488 files
2 changes: 1 addition & 1 deletion include/torch-mlir/Conversion/TorchToTosa/TosaLegalizeUtils.h
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@ namespace tosa {
// rounding mode
Value buildRescale(PatternRewriter &rewriter, Operation *op,
ShapedType output_type, Value input_val, double scale,
int64_t input_zp, int64_t output_zp, bool double_round,
int64_t input_zp, int64_t output_zp, StringRef rounding_mode,
bool scale32);

// Creates TOSA rescale op with int32 output
Expand Down
2 changes: 1 addition & 1 deletion lib/Conversion/TorchToTosa/TosaLegalizeCommon.cpp
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Expand Up @@ -777,7 +777,7 @@ std::optional<Value> convertReduceOpCommon(
RankedTensorType output_rescale_type =
RankedTensorType::get(shape_vec, output_type.getElementType());
val = buildRescale(rewriter, op, output_rescale_type, val, output_scale,
0, output_zp, false, true);
0, output_zp, "SINGLE_ROUND", true);
}

// Optionally squeeze out the reduced axes.
Expand Down
64 changes: 44 additions & 20 deletions lib/Conversion/TorchToTosa/TosaLegalizeUtils.cpp
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Expand Up @@ -34,14 +34,15 @@ Value buildRescaleMultiplier(bool scale32, PatternRewriter &rewriter,
// rounding mode
Value buildRescale(PatternRewriter &rewriter, Operation *op,
ShapedType output_type, Value input_val, double scale,
int64_t input_zp, int64_t output_zp, bool double_round,
int64_t input_zp, int64_t output_zp, StringRef rounding_mode,
bool scale32) {
int32_t multiplier;
int32_t shift;

int32_t scale_width = scale32 ? 32 : 16;

computeMultiplierAndShift(scale, multiplier, shift, scale_width);
if (!computeMultiplierAndShift(scale, multiplier, shift, scale_width))
op->emitError("buildRescale: shift must be in the range 2 <= shift <= 62");

Value multiplier_val =
buildRescaleMultiplier(scale32, rewriter, op, {multiplier});
Expand All @@ -52,11 +53,23 @@ Value buildRescale(PatternRewriter &rewriter, Operation *op,
bool input_unsigned = input_val.getType().isUnsignedInteger();
bool output_unsigned = output_type.isUnsignedInteger();

// Create input_zp matches the input type and output_zp matches the output
// type of RescaleOp
const auto input_zp_val = tosa::createZeroPointTensor(
rewriter, op->getLoc(), dyn_cast<TensorType>(input_val.getType()),
input_zp);
if (!input_zp_val.has_value())
op->emitError("Failed to create input zero-point tensor for RescaleOp.");

const auto output_zp_val = tosa::createZeroPointTensor(
rewriter, op->getLoc(), output_type, output_zp);
if (!output_zp_val.has_value())
op->emitError("Failed to create output zero-point tensor for RescaleOp.");

auto rescale_op = CreateOpAndInfer<tosa::RescaleOp>(
rewriter, op->getLoc(), output_type, input_val, multiplier_val, shift_val,
rewriter.getI32IntegerAttr(static_cast<int32_t>(input_zp)),
rewriter.getI32IntegerAttr(static_cast<int32_t>(output_zp)),
rewriter.getBoolAttr(scale32), rewriter.getBoolAttr(double_round),
input_zp_val.value(), output_zp_val.value(),
rewriter.getBoolAttr(scale32), rewriter.getStringAttr(rounding_mode),
rewriter.getBoolAttr(false), rewriter.getBoolAttr(input_unsigned),
rewriter.getBoolAttr(output_unsigned));

Expand All @@ -73,7 +86,7 @@ Value buildRescaleToInt32(PatternRewriter &rewriter, Operation *op,
auto output_type = input_type.clone(rewriter.getI32Type());

return buildRescale(rewriter, op, output_type, input_val, input_scale,
input_zp, 0, false, true);
input_zp, 0, "SINGLE_ROUND", true);
}

// Creates a TOSA rescale op based on conv2d parameters.
Expand All @@ -96,6 +109,16 @@ Value buildRescaleOpConvOutput(PatternRewriter &rewriter, Operation *op,
bool input_unsigned = input_qtype.isUnsignedInteger();
bool output_unsigned = output_qtype.isUnsignedInteger();

const auto input_zp_val = tosa::createZeroPointTensor(
rewriter, op->getLoc(), input_type, static_cast<int64_t>(0));
if (!input_zp_val.has_value())
op->emitError("Failed to create input zero-point tensor for RescaleOp.");

const auto output_zp_val = tosa::createZeroPointTensor(
rewriter, op->getLoc(), output_type, output_zp);
if (!output_zp_val.has_value())
op->emitError("Failed to create output zero-point tensor for RescaleOp.");

if (auto weight_per_tensor_qtype =
dyn_cast<mlir::quant::UniformQuantizedType>(
weight_type.getElementType())) {
Expand All @@ -107,7 +130,11 @@ Value buildRescaleOpConvOutput(PatternRewriter &rewriter, Operation *op,

double op_tensor_scale = (input_scale * weight_scale) / output_scale;

computeMultiplierAndShift(op_tensor_scale, multiplier, shift, scale_width);
if (!computeMultiplierAndShift(op_tensor_scale, multiplier, shift,
scale_width))
op->emitError(
"buildRescaleOpConvOutput: shift must be in the range 2 <= shift <= "
"62");

Value multiplier_val =
buildRescaleMultiplier(scale32, rewriter, op, {multiplier});
Expand All @@ -117,10 +144,9 @@ Value buildRescaleOpConvOutput(PatternRewriter &rewriter, Operation *op,

auto rescale_op = CreateOpAndInfer<tosa::RescaleOp>(
rewriter, op->getLoc(), output_type, conv_val, multiplier_val,
shift_val, rewriter.getI32IntegerAttr(0),
rewriter.getI32IntegerAttr(output_zp), rewriter.getBoolAttr(scale32),
rewriter.getBoolAttr(true), rewriter.getBoolAttr(false),
rewriter.getBoolAttr(input_unsigned),
shift_val, input_zp_val.value(), output_zp_val.value(),
rewriter.getBoolAttr(scale32), rewriter.getStringAttr("DOUBLE_ROUND"),
rewriter.getBoolAttr(false), rewriter.getBoolAttr(input_unsigned),
rewriter.getBoolAttr(output_unsigned));

return rescale_op.getResult();
Expand All @@ -136,17 +162,16 @@ Value buildRescaleOpConvOutput(PatternRewriter &rewriter, Operation *op,
weight_per_channel_qtype.getScales().begin(),
weight_per_channel_qtype.getScales().end());

int64_t output_zp = output_qtype.getZeroPoint();
double output_scale = output_qtype.getScale();

for (double weight_scale : weight_scale_arr) {
int32_t multiplier;
int32_t shift;

double op_channel_scale = (input_scale * weight_scale) / output_scale;

computeMultiplierAndShift(op_channel_scale, multiplier, shift,
scale_width);
if (!computeMultiplierAndShift(op_channel_scale, multiplier, shift, 32))
op->emitError(
"buildRescaleOpConvOutput: shift must be in the range 2 <= shift "
"<= 62");

multiplier_arr.push_back(multiplier);
shift_arr.push_back(static_cast<int8_t>(shift));
Expand All @@ -161,10 +186,9 @@ Value buildRescaleOpConvOutput(PatternRewriter &rewriter, Operation *op,

auto rescale_op = CreateOpAndInfer<tosa::RescaleOp>(
rewriter, op->getLoc(), output_type, conv_val, multiplier_val,
shift_val, rewriter.getI32IntegerAttr(0),
rewriter.getI32IntegerAttr(output_zp), rewriter.getBoolAttr(scale32),
rewriter.getBoolAttr(true), rewriter.getBoolAttr(true),
rewriter.getBoolAttr(input_unsigned),
shift_val, input_zp_val.value(), output_zp_val.value(),
rewriter.getBoolAttr(scale32), rewriter.getStringAttr("DOUBLE_ROUND"),
rewriter.getBoolAttr(true), rewriter.getBoolAttr(input_unsigned),
rewriter.getBoolAttr(output_unsigned));

return rescale_op.getResult();
Expand Down
4 changes: 4 additions & 0 deletions projects/pt1/e2e_testing/xfail_sets.py
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Expand Up @@ -1717,6 +1717,8 @@
"ScatterSrcModule_basic",
"ScatterSrcStaticModule_basic",
"HBC_basic",
# 1D inputs cause generated tosa.negate ops to crash downstream
"NllLossModule_1D_basic",
}

# Write the TOSA set as a "passing" set as it is very early in development
Expand Down Expand Up @@ -3364,6 +3366,8 @@
}

FX_IMPORTER_TOSA_XFAIL_SET = {
"NumpyTRank0Module_basic",
"Permute0RankModule_basic",
"ArgsortTensor_basic",
"ArgsortTensorInteger_basic",
"AtenSymConstrainRangeForSize_basic",
Expand Down
31 changes: 18 additions & 13 deletions test/Conversion/TorchToTosa/basic.mlir
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Expand Up @@ -94,11 +94,14 @@ func.func @torch.aten.exp$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vten
// -----

// CHECK-LABEL: func.func @torch.aten.neg$basic(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[ARG_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[RESULT_BUILTIN:.*]] = tosa.negate %[[ARG_BUILTIN]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,?],f32>
// CHECK-SAME: %[[VAL_0:[0-9]+|[a-zA-Z$._-][a-zA-Z0-9$._-]*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
// CHECK: %[[VAL_3:.*]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
// CHECK: %[[VAL_4:.*]] = tosa.negate %[[VAL_1]], %[[VAL_2]], %[[VAL_3]] : (tensor<?x?xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func.func @torch.aten.neg$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.neg %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
Expand Down Expand Up @@ -1555,20 +1558,22 @@ func.func @torch.aten.tril$basic(%arg0: !torch.vtensor<[2,4], si32>) -> !torch.v
// -----

// CHECK-LABEL: func.func @torch.aten.min.dim$basic(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<3x2x3xf32>) -> tensor<3x2x1xf32> {
// CHECK-SAME: %[[VAL_0:[0-9]+|[a-zA-Z$._-][a-zA-Z0-9$._-]*]]: tensor<3x2x3xf32>) -> tensor<3x2x1xf32> {
// CHECK: %[[VAL_1:.*]] = torch_c.from_builtin_tensor %[[VAL_0]] : tensor<3x2x3xf32> -> !torch.vtensor<[3,2,3],f32>
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[3,2,3],f32> -> tensor<3x2x3xf32>
// CHECK: %[[VAL_3:.*]] = torch.constant.bool true
// CHECK: %[[VAL_4:.*]] = torch.constant.int 2
// CHECK: %[[VAL_5:.*]] = tosa.const_shape {values = dense<[3, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
// CHECK-DAG: %[[VAL_5:.*]] = tosa.const_shape {values = dense<[3, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
// CHECK: %[[VAL_6:.*]] = tosa.reduce_min %[[VAL_2]] {axis = 2 : i32} : (tensor<3x2x3xf32>) -> tensor<3x2x1xf32>
// CHECK: %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<3x2x1xf32> -> !torch.vtensor<[3,2,1],f32>
// CHECK: %[[VAL_8:.*]] = tosa.negate %[[VAL_2]] : (tensor<3x2x3xf32>) -> tensor<3x2x3xf32>
// CHECK: %[[VAL_9:.*]] = tosa.argmax %[[VAL_8]] {axis = 2 : i32} : (tensor<3x2x3xf32>) -> tensor<3x2xi64>
// CHECK: %[[VAL_10:.*]] = tosa.const_shape {values = dense<[3, 2, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
// CHECK: %[[VAL_11:.*]] = tosa.reshape %[[VAL_9]], %[[VAL_10]] : (tensor<3x2xi64>, !tosa.shape<3>) -> tensor<3x2x1xi64>
// CHECK: %[[VAL_12:.*]] = torch_c.to_builtin_tensor %[[VAL_7]] : !torch.vtensor<[3,2,1],f32> -> tensor<3x2x1xf32>
// CHECK: return %[[VAL_12]] : tensor<3x2x1xf32>
// CHECK: %[[VAL_8:.*]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
// CHECK: %[[VAL_9:.*]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
// CHECK: %[[VAL_10:.*]] = tosa.negate %[[VAL_2]], %[[VAL_8]], %[[VAL_9]] : (tensor<3x2x3xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<3x2x3xf32>
// CHECK: %[[VAL_11:.*]] = tosa.argmax %[[VAL_10]] {axis = 2 : i32} : (tensor<3x2x3xf32>) -> tensor<3x2xi64>
// CHECK: %[[VAL_12:.*]] = tosa.const_shape {values = dense<[3, 2, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
// CHECK: %[[VAL_13:.*]] = tosa.reshape %[[VAL_11]], %[[VAL_12]] : (tensor<3x2xi64>, !tosa.shape<3>) -> tensor<3x2x1xi64>
// CHECK: %[[VAL_14:.*]] = torch_c.to_builtin_tensor %[[VAL_7]] : !torch.vtensor<[3,2,1],f32> -> tensor<3x2x1xf32>
// CHECK: return %[[VAL_14]] : tensor<3x2x1xf32>
// CHECK: }
func.func @torch.aten.min.dim$basic(%arg0: tensor<3x2x3xf32>) -> tensor<3x2x1xf32> {
%0 = torch_c.from_builtin_tensor %arg0 : tensor<3x2x3xf32> -> !torch.vtensor<[3,2,3],f32>
Expand Down
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