[Mlir-commits] [mlir] 02bf3b5 - [mlir][linalg] Add quantized conv2d operator with FCHW, NCHW order (#107740)
llvmlistbot at llvm.org
llvmlistbot at llvm.org
Sat Oct 19 09:25:30 PDT 2024
Author: Felix Schneider
Date: 2024-10-19T18:25:27+02:00
New Revision: 02bf3b54c02643069ad1a952c19f97cab00a3241
URL: https://github.com/llvm/llvm-project/commit/02bf3b54c02643069ad1a952c19f97cab00a3241
DIFF: https://github.com/llvm/llvm-project/commit/02bf3b54c02643069ad1a952c19f97cab00a3241.diff
LOG: [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.
Added:
Modified:
mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
mlir/test/Dialect/Linalg/roundtrip.mlir
Removed:
################################################################################
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
index 8cb698096ef5b7..bf2f26de26e9ed 100644
--- 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
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 e4a6ec7487bb2f..b45fecd0ee1457 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
@@ -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))
+ at 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),
diff --git a/mlir/test/Dialect/Linalg/roundtrip.mlir b/mlir/test/Dialect/Linalg/roundtrip.mlir
index 146e9780b8ebbe..1b8969bd115595 100644
--- 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>
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