[mlir][linalg] Add quantized conv2d operator with FCHW,NCHW order (#107740)

This patch adds a quantized version of the `linalg.conv2d_nchw_fchw` Op. This is the "channel-first" ordering typically used by PyTorch and others.
2024-10-19 18:25:27 +02:00
parent 0f0a96b862
commit 02bf3b54c0
3 changed files with 196 additions and 0 deletions
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
@@ -3114,6 +3114,143 @@ structured_op: !LinalgStructuredOpConfig
                    - !ScalarExpression
                      scalar_arg: KZp
 --- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+  name: conv_2d_nchw_fchw_q
+  cpp_class_name: Conv2DNchwFchwQOp
+  doc: |-
+    Performs 2-D convolution with zero point offsets.
+
+    Layout:
+      * Input: NCHW.
+      * Kernel: FCHW.
+
+    Numeric casting is performed on the operands to the inner multiply, promoting
+    them to the same data type as the accumulator/output. This includes the zero
+    point offsets common to quantized operations.
+  implements:
+  - LinalgConvolutionOpInterface
+structured_op: !LinalgStructuredOpConfig
+  args:
+  - !LinalgOperandDefConfig
+    name: I
+    kind: input_tensor
+    type_var: T1
+    shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10] -> (s0,
+      s1, s2 * s3 + s4 * s5, s6 * s7 + s8 * s9)>
+  - !LinalgOperandDefConfig
+    name: K
+    kind: input_tensor
+    type_var: T2
+    shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10] -> (s10,
+      s1, s4, s8)>
+  - !LinalgOperandDefConfig
+    name: IZp
+    kind: scalar
+    type_var: I32
+  - !LinalgOperandDefConfig
+    name: KZp
+    kind: scalar
+    type_var: I32
+  - !LinalgOperandDefConfig
+    name: O
+    kind: output_tensor
+    type_var: U
+    shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10] -> (s0,
+      s10, s2, s6)>
+  - !LinalgOperandDefConfig
+    name: strides
+    kind: index_attr
+    index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10] ->
+      (s3, s7)>
+    default_indices:
+    - 1
+    - 1
+  - !LinalgOperandDefConfig
+    name: dilations
+    kind: index_attr
+    index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10] ->
+      (s5, s9)>
+    default_indices:
+    - 1
+    - 1
+  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] -> (d0, d4, d2 * s3 + d5 * s5, d3 * s7 + d6 * s9)>
+    - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+      s9, s10] -> (d1, d4, d5, d6)>
+    - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+      s9, s10] -> ()>
+    - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+      s9, s10] -> ()>
+    - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+      s9, s10] -> (d0, d1, d2, d3)>
+  iterator_types:
+  - parallel
+  - parallel
+  - parallel
+  - parallel
+  - reduction
+  - reduction
+  - reduction
+  assignments:
+  - !ScalarAssign
+    arg: O
+    value: !ScalarExpression
+      scalar_fn:
+        kind: binary
+        fn_name: add
+        operands:
+        - !ScalarExpression
+          scalar_arg: O
+        - !ScalarExpression
+          scalar_fn:
+            kind: binary
+            fn_name: mul
+            operands:
+            - !ScalarExpression
+              scalar_fn:
+                kind: binary
+                fn_name: sub
+                operands:
+                - !ScalarExpression
+                  scalar_fn:
+                    kind: type
+                    fn_name: cast_signed
+                    type_var: U
+                    operands:
+                    - !ScalarExpression
+                      scalar_arg: I
+                - !ScalarExpression
+                  scalar_fn:
+                    kind: type
+                    fn_name: cast_signed
+                    type_var: U
+                    operands:
+                    - !ScalarExpression
+                      scalar_arg: IZp
+            - !ScalarExpression
+              scalar_fn:
+                kind: binary
+                fn_name: sub
+                operands:
+                - !ScalarExpression
+                  scalar_fn:
+                    kind: type
+                    fn_name: cast_signed
+                    type_var: U
+                    operands:
+                    - !ScalarExpression
+                      scalar_arg: K
+                - !ScalarExpression
+                  scalar_fn:
+                    kind: type
+                    fn_name: cast_signed
+                    type_var: U
+                    operands:
+                    - !ScalarExpression
+                      scalar_arg: KZp
+--- !LinalgOpConfig
 metadata: !LinalgOpMetadata
  name: conv_2d_nchw_fchw
  cpp_class_name: Conv2DNchwFchwOp
--- a/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
+++ b/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
@@ -876,6 +876,35 @@ def conv_2d_nhwc_fhwc_q(
    ) * (TypeFn.cast_signed(U, K[D.f, D.kh, D.kw, D.c]) - TypeFn.cast_signed(U, KZp))


+@linalg_structured_op
+def conv_2d_nchw_fchw_q(
+    I=TensorDef(T1, S.N, S.C, S.OH * S.SH + S.KH * S.DH, S.OW * S.SW + S.KW * S.DW),
+    K=TensorDef(T2, S.F, S.C, S.KH, S.KW),
+    IZp=ScalarDef(I32),
+    KZp=ScalarDef(I32),
+    O=TensorDef(U, S.N, S.F, S.OH, S.OW, output=True),
+    strides=IndexAttrDef(S.SH, S.SW, default=[1, 1]),
+    dilations=IndexAttrDef(S.DH, S.DW, default=[1, 1]),
+):
+    """Performs 2-D convolution with zero point offsets.
+
+    Layout:
+      * Input: NCHW.
+      * Kernel: FCHW.
+
+    Numeric casting is performed on the operands to the inner multiply, promoting
+    them to the same data type as the accumulator/output. This includes the zero
+    point offsets common to quantized operations.
+    """
+    implements(ConvolutionOpInterface)
+    domain(D.n, D.f, D.oh, D.ow, D.c, D.kh, D.kw)
+    O[D.n, D.f, D.oh, D.ow] += (
+        TypeFn.cast_signed(
+            U, I[D.n, D.c, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW]
+        )
+        - TypeFn.cast_signed(U, IZp)
+    ) * (TypeFn.cast_signed(U, K[D.f, D.c, D.kh, D.kw]) - TypeFn.cast_signed(U, KZp))
+
@linalg_structured_op
 def conv_2d_nchw_fchw(
    I=TensorDef(T1, S.N, S.C, S.OH * S.SH + S.KH * S.DH, S.OW * S.SW + S.KW * S.DW),
--- a/mlir/test/Dialect/Linalg/roundtrip.mlir
+++ b/mlir/test/Dialect/Linalg/roundtrip.mlir
@@ -664,3 +664,33 @@ func.func @winograd_output_dyn(%arg0: tensor<6x6x?x?x?x?xf32>, %arg1: tensor<?x?

 // CHECK-LABEL: func @winograd_output_dyn
 // CHECK:         linalg.winograd_output_transform m(4) r(3) ins(%arg0 : tensor<6x6x?x?x?x?xf32>) outs(%arg1 : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
+
+// -----
+
+func.func @conv2d_channel_first_q(%img: tensor<100x3x224x224xi32>, %filt: tensor<64x3x5x5xi32>, %a: i32, %b: i32) -> tensor<100x64x220x220xi32> {
+  %init = arith.constant dense<0> : tensor<100x64x220x220xi32>
+  %1 = linalg.conv_2d_nchw_fchw_q  {dilations = dense<1> : tensor<2xi64>,
+      strides = dense<1> : tensor<2xi64>}
+    ins(%img, %filt, %a, %b : tensor<100x3x224x224xi32>, tensor<64x3x5x5xi32>, i32, i32)
+    outs(%init : tensor<100x64x220x220xi32>) -> tensor<100x64x220x220xi32>
+  return %1 : tensor<100x64x220x220xi32>
+}
+
+// CHECK-LABEL: func @conv2d_channel_first_q(
+// CHECK:   %[[arg0:[a-zA-z0-9]*]]: tensor<100x3x224x224xi32>, %[[arg1:[a-zA-z0-9]*]]: tensor<64x3x5x5xi32>, %[[arg2:[a-zA-z0-9]*]]: i32, %[[arg3:[a-zA-z0-9]*]]: i32)
+// CHECK:         linalg.conv_2d_nchw_fchw_q {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%[[arg0]], %[[arg1]], %[[arg2]], %[[arg3]] : tensor<100x3x224x224xi32>, tensor<64x3x5x5xi32>, i32, i32) outs(%{{.*}} : tensor<100x64x220x220xi32>) -> tensor<100x64x220x220xi32>
+
+// -----
+
+func.func @conv2d_channel_first_q_promote(%img: tensor<100x3x224x224xi8>, %filt: tensor<64x3x5x5xi8>, %a: i8, %b: i8) -> tensor<100x64x220x220xi32> {
+  %init = arith.constant dense<0> : tensor<100x64x220x220xi32>
+  %1 = linalg.conv_2d_nchw_fchw_q  {dilations = dense<1> : tensor<2xi64>,
+      strides = dense<1> : tensor<2xi64>}
+    ins(%img, %filt, %a, %b : tensor<100x3x224x224xi8>, tensor<64x3x5x5xi8>, i8, i8)
+    outs(%init : tensor<100x64x220x220xi32>) -> tensor<100x64x220x220xi32>
+  return %1 : tensor<100x64x220x220xi32>
+}
+
+// CHECK-LABEL: func @conv2d_channel_first_q_promote(
+// CHECK:   %[[arg0:[a-zA-z0-9]*]]: tensor<100x3x224x224xi8>, %[[arg1:[a-zA-z0-9]*]]: tensor<64x3x5x5xi8>, %[[arg2:[a-zA-z0-9]*]]: i8, %[[arg3:[a-zA-z0-9]*]]: i8)
+// CHECK:         linalg.conv_2d_nchw_fchw_q {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%[[arg0]], %[[arg1]], %[[arg2]], %[[arg3]] : tensor<100x3x224x224xi8>, tensor<64x3x5x5xi8>, i8, i8) outs(%{{.*}} : tensor<100x64x220x220xi32>) -> tensor<100x64x220x220xi32>