[Mlir-commits] [mlir] cf8a1f6 - [mlir][tosa] Quantized Conv2DOp lowering to linalg added.
Rob Suderman
llvmlistbot at llvm.org
Thu Jul 22 15:43:37 PDT 2021
Author: Rob Suderman
Date: 2021-07-22T15:42:26-07:00
New Revision: cf8a1f62083c3edbf2cd08bb16d57a70dc45722c
URL: https://github.com/llvm/llvm-project/commit/cf8a1f62083c3edbf2cd08bb16d57a70dc45722c
DIFF: https://github.com/llvm/llvm-project/commit/cf8a1f62083c3edbf2cd08bb16d57a70dc45722c.diff
LOG: [mlir][tosa] Quantized Conv2DOp lowering to linalg added.
Includes a version of a quantized conv2D operations with a lowering from TOSA
to linalg with corresponding test. We keep the quantized and quantized variants
as separate named ops to avoid the additional operations for non-quantized
convolutions.
Differential Revision: https://reviews.llvm.org/D106407
Added:
Modified:
mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
mlir/test/Dialect/Linalg/named-ops.mlir
Removed:
################################################################################
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
index 7e07275173a05..62f90b8629875 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
@@ -709,6 +709,121 @@ structured_op: !LinalgStructuredOpConfig
- !ScalarExpression
scalar_arg: K
--- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+ name: conv_2d_input_nhwc_filter_ohwi_poly_q
+ cpp_class_name: Conv2DInputNhwcFilterOhwiPolyQOp
+ doc: |-
+ Performs a 2-D quantized convolution.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output. Includes zero point
+ adjustment for quantization.
+structured_op: !LinalgStructuredOpConfig
+ args:
+ - !LinalgOperandDefConfig
+ name: I
+ usage: InputOperand
+ type_var: T1
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12]
+ -> (s0, s1, s2, s3)>
+ - !LinalgOperandDefConfig
+ name: K
+ usage: InputOperand
+ type_var: T2
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12]
+ -> (s4, s5, s6, s3)>
+ - !LinalgOperandDefConfig
+ name: IZp
+ usage: InputOperand
+ type_var: I32
+ - !LinalgOperandDefConfig
+ name: KZp
+ usage: InputOperand
+ type_var: I32
+ - !LinalgOperandDefConfig
+ name: O
+ usage: OutputOperand
+ type_var: U
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12]
+ -> (s0, s7, s8, s4)>
+ - !LinalgOperandDefConfig
+ name: strides
+ usage: IndexAttribute
+ type_var: I64
+ attribute_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11,
+ s12] -> (s9, s10)>
+ - !LinalgOperandDefConfig
+ name: dilations
+ usage: IndexAttribute
+ type_var: I64
+ attribute_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11,
+ s12] -> (s11, s12)>
+ indexing_maps: !LinalgIndexingMapsConfig
+ static_indexing_maps:
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+ s9, s10, s11, s12] -> (d0, d1 * s9 + d3 * s11, d2 * s10 + d4 * s12, d6)>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+ s9, s10, s11, s12] -> (d5, d3, d4, d6)>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+ s9, s10, s11, s12] -> ()>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+ s9, s10, s11, s12] -> ()>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+ s9, s10, s11, s12] -> (d0, d1, d2, d5)>
+ iterator_types:
+ - parallel
+ - parallel
+ - parallel
+ - reduction
+ - reduction
+ - parallel
+ - reduction
+ assignments:
+ - !ScalarAssign
+ arg: O
+ value: !ScalarExpression
+ scalar_apply:
+ fn_name: add
+ operands:
+ - !ScalarExpression
+ scalar_arg: O
+ - !ScalarExpression
+ scalar_apply:
+ fn_name: mul
+ operands:
+ - !ScalarExpression
+ scalar_apply:
+ fn_name: sub
+ operands:
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: I
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: IZp
+ - !ScalarExpression
+ scalar_apply:
+ fn_name: sub
+ operands:
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: K
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: KZp
+--- !LinalgOpConfig
metadata: !LinalgOpMetadata
name: depthwise_conv_2d_input_nhwc_filter_hwc_poly
cpp_class_name: DepthwiseConv2DInputNhwcFilterHwcPolyOp
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
index 6509df285a890..e44b2457e9250 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
@@ -862,14 +862,24 @@ convolutionMatchAndRewriterHelper(Operation *op,
ShapedType resultTy = op->getResult(0).getType().cast<ShapedType>();
Type inputETy = inputTy.getElementType();
- Type weightETy = weightTy.getElementType();
- Type biasETy = biasTy.getElementType();
Type resultETy = resultTy.getElementType();
auto padAttr = op->getAttr("pad").cast<ArrayAttr>();
auto strideTosaAttr = op->getAttr("stride").cast<ArrayAttr>();
auto dilationTosaAttr = op->getAttr("dilation").cast<ArrayAttr>();
+ bool isQuantized = op->hasAttr("quantization_info");
+ IntegerAttr iZp;
+ IntegerAttr kZp;
+ if (isQuantized) {
+ auto quantizationInfo =
+ op->getAttr("quantization_info").cast<tosa::ConvOpQuantizationAttr>();
+ iZp = rewriter.getI32IntegerAttr(
+ quantizationInfo.input_zp().getValue().getSExtValue());
+ kZp = rewriter.getI32IntegerAttr(
+ quantizationInfo.weight_zp().getValue().getSExtValue());
+ }
+
if (!inputTy.hasStaticShape() || !weightTy.hasStaticShape() ||
!biasTy.hasStaticShape() || !resultTy.hasStaticShape())
return rewriter.notifyMatchFailure(op,
@@ -878,11 +888,6 @@ convolutionMatchAndRewriterHelper(Operation *op,
auto weightShape = weightTy.getShape();
auto resultShape = resultTy.getShape();
- // TODO(suderman): Support other types.
- if (!inputETy.isF32() || !weightETy.isF32() || !biasETy.isF32() ||
- !resultETy.isF32())
- return failure();
-
// Apply padding as necessary.
Attribute zeroAttr = rewriter.getZeroAttr(inputETy);
llvm::SmallVector<int64_t> pad;
@@ -924,14 +929,23 @@ convolutionMatchAndRewriterHelper(Operation *op,
auto dilationAttr = DenseIntElementsAttr::get(
RankedTensorType::get({2}, rewriter.getI64Type()), dilation);
- if (isa<tosa::Conv2DOp>(op)) {
+ if (isa<tosa::Conv2DOp>(op) && !isQuantized) {
rewriter.replaceOpWithNewOp<linalg::Conv2DInputNhwcFilterOhwiPolyOp>(
op, resultTy, ValueRange{input, weight}, ValueRange{biasBroadcast},
strideAttr, dilationAttr);
return success();
}
- if (isa<tosa::DepthwiseConv2DOp>(op)) {
+ if (isa<tosa::Conv2DOp>(op) && isQuantized) {
+ auto iZpVal = rewriter.create<ConstantOp>(loc, iZp);
+ auto kZpVal = rewriter.create<ConstantOp>(loc, kZp);
+ rewriter.replaceOpWithNewOp<linalg::Conv2DInputNhwcFilterOhwiPolyQOp>(
+ op, resultTy, ValueRange{input, weight, iZpVal, kZpVal},
+ ValueRange{biasBroadcast}, strideAttr, dilationAttr);
+ return success();
+ }
+
+ if (isa<tosa::DepthwiseConv2DOp>(op) && !isQuantized) {
ShapedType linalgConvTy =
RankedTensorType::get({resultShape[0], resultShape[1], resultShape[2],
weightShape[2], weightShape[3]},
diff --git a/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py b/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
index ebe9822b16630..cbb2c0e312618 100644
--- a/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
+++ b/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
@@ -164,6 +164,30 @@ def conv_2d_input_nhwc_filter_ohwi_poly(
D.ow * S.SW + D.kw * S.DW,
D.ic]) * cast(U, K[D.oc, D.kh, D.kw, D.ic])
+ at linalg_structured_op
+def conv_2d_input_nhwc_filter_ohwi_poly_q(
+ I=TensorDef(T1, S.N, S.IH, S.IW, S.IC),
+ K=TensorDef(T2, S.OC, S.KH, S.KW, S.IC),
+ IZp=ScalarDef(I32),
+ KZp=ScalarDef(I32),
+ O=TensorDef(U, S.N, S.OH, S.OW, S.OC, output=True),
+ strides=AttributeDef(S.SH, S.SW),
+ dilations=AttributeDef(S.DH, S.DW)):
+ """Performs a 2-D quantized convolution.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output. Includes zero point
+ adjustment for quantization.
+ """
+ domain(D.n, D.oh, D.ow, D.kh, D.kw, D.oc, D.ic)
+ O[D.n, D.oh, D.ow, D.oc] += ((cast(
+ U, I[D.n,
+ D.oh * S.SH + D.kh * S.DH,
+ D.ow * S.SW + D.kw * S.DW,
+ D.ic]) - cast(U, IZp)) *
+ (cast(U, K[D.oc, D.kh, D.kw, D.ic]) - cast(U, KZp)))
+
+
@linalg_structured_op
def depthwise_conv_2d_input_nhwc_filter_hwc_poly(
I=TensorDef(T1, S.N, S.IH, S.IW, S.C),
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
index 38a70fb5f5e49..59454f2328a59 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
@@ -1184,17 +1184,21 @@ func @avg_pool(%arg0: tensor<1x6x34x62xf32>) -> (tensor<1x5x33x62xf32>) {
// -----
-// CHECK: #[[$MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d3)>
-// CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d3)>
+// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+// CHECK-LABEL: @conv2d_f32
func @conv2d_f32(%input: tensor<1x49x42x27xf32>, %weights: tensor<28x3x3x27xf32>, %bias: tensor<28xf32>) -> () {
// CHECK: %[[INIT:.+]] = linalg.init_tensor [1, 45, 40, 28]
- // CHECK: %[[BROADCAST:.+]] = linalg.generic {indexing_maps = [#[[$MAP2]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<28xf32>) outs(%[[INIT]] : tensor<1x45x40x28xf32>)
+ // CHECK: %[[BROADCAST:.+]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<28xf32>) outs(%[[INIT]] : tensor<1x45x40x28xf32>)
// CHECK: linalg.conv_2d_input_nhwc_filter_ohwi_poly {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>) outs(%[[BROADCAST]] : tensor<1x45x40x28xf32>)
%0 = "tosa.conv2d"(%input, %weights, %bias) {pad = [0, 0, 0, 0], stride = [1, 1], dilation = [2, 1]} : (tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>, tensor<28xf32>) -> (tensor<1x45x40x28xf32>)
return
}
+// -----
+
+// CHECK-LABEL: @conv2d_padded_f32
func @conv2d_padded_f32(%input: tensor<1x47x40x28xf32>, %weights: tensor<28x3x3x28xf32>, %bias: tensor<28xf32>) -> () {
// CHECK: linalg.pad_tensor %arg0
// CHECK: linalg.conv_2d_input_nhwc_filter_ohwi_poly
@@ -1204,6 +1208,24 @@ func @conv2d_padded_f32(%input: tensor<1x47x40x28xf32>, %weights: tensor<28x3x3x
// -----
+// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d3)>
+// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+
+// CHECK-LABEL: @conv2d_quant
+func @conv2d_quant(%arg0 : tensor<1x12x12x1xi8>, %arg1 : tensor<1024x3x3x1xi8>, %arg2 : tensor<1024xi32>) -> () {
+ // CHECK: %[[INIT:.+]] = linalg.init_tensor [1, 10, 10, 1024]
+ // CHECK: %[[BROADCAST:.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<1024xi32>) outs(%[[INIT]] : tensor<1x10x10x1024xi32>)
+ // CHECK: ^bb0(%arg3: i32, %arg4: i32):
+ // CHECK: linalg.yield %arg3 : i32
+ // CHECK: %[[C128:.+]] = constant -128
+ // CHECK: %[[C42:.+]] = constant 42
+ // CHECK: linalg.conv_2d_input_nhwc_filter_ohwi_poly_q {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1, %[[C128]], %[[C42]] : tensor<1x12x12x1xi8>, tensor<1024x3x3x1xi8>, i32, i32) outs(%1 : tensor<1x10x10x1024xi32>)
+ %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [0, 0, 0, 0], quantization_info = {input_zp = -128 : i32, weight_zp = 42 : i32}, stride = [1, 1]} : (tensor<1x12x12x1xi8>, tensor<1024x3x3x1xi8>, tensor<1024xi32>) -> tensor<1x10x10x1024xi32>
+ return
+}
+
+// -----
+
// CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d3)>
// CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
diff --git a/mlir/test/Dialect/Linalg/named-ops.mlir b/mlir/test/Dialect/Linalg/named-ops.mlir
index db5d4c6c9977d..c873f66e2a652 100644
--- a/mlir/test/Dialect/Linalg/named-ops.mlir
+++ b/mlir/test/Dialect/Linalg/named-ops.mlir
@@ -1,5 +1,19 @@
// RUN: mlir-opt -split-input-file -verify-diagnostics %s | FileCheck %s
+// CHECK-LABEL: func @conv_2d_input_nhwc_filter_ohwi_poly_q_tensor
+func @conv_2d_input_nhwc_filter_ohwi_poly_q_tensor(%input: tensor<2x4x5x3xi8>, %filter: tensor<2x2x2x3xi8>) -> tensor<2x3x4x2xi32> {
+ %zero = constant 0 : i32
+ %init = linalg.init_tensor [2, 3, 4, 2] : tensor<2x3x4x2xi32>
+ %fill = linalg.fill(%zero, %init) : i32, tensor<2x3x4x2xi32> -> tensor<2x3x4x2xi32>
+ %c128 = constant -128 : i32
+ %c42 = constant 42 : i32
+ %0 = linalg.conv_2d_input_nhwc_filter_ohwi_poly_q
+ { dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64> }
+ ins(%input, %filter, %c128, %c42 : tensor<2x4x5x3xi8>, tensor<2x2x2x3xi8>, i32, i32)
+ outs(%fill : tensor<2x3x4x2xi32>) -> tensor<2x3x4x2xi32>
+ return %0 : tensor<2x3x4x2xi32>
+}
+
// CHECK-LABEL: func @depthwise_conv_2d_input_nhwc_filter_hwcf_tensor
func @depthwise_conv_2d_input_nhwc_filter_hwcf_tensor(%input: tensor<2x4x5x2xf32>, %filter: tensor<2x2x2x3xf32>) -> tensor<2x3x4x2x3xf32> {
%zero = constant 0.000000e+00 : f32
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