[Mlir-commits] [mlir] [linalg] Add quantized version of `conv_3d_ncdhw_fcdhw` (PR #113953)
Felix Schneider
llvmlistbot at llvm.org
Mon Oct 28 12:25:45 PDT 2024
https://github.com/ubfx created https://github.com/llvm/llvm-project/pull/113953
This patch adds the quantized 3d convolution operator `conv_3d_ncdhw_fcdhw_q`. This is the "channel-first" dimension ordering used by PyTorch and others.
>From 8ff731c832de797c8680b57c94dce67e526d3608 Mon Sep 17 00:00:00 2001
From: Felix Schneider <fx.schn at gmail.com>
Date: Mon, 28 Oct 2024 20:22:23 +0100
Subject: [PATCH] [linalg] Add quantized version of `conv_3d_ncdhw_fcdhw`
This patch adds the quantized 3d convolution operator `conv_3d_ncdhw_fcdhw_q`.
This is the "channel-first" dimension ordering used by PyTorch and others.
---
.../Linalg/IR/LinalgNamedStructuredOps.yaml | 139 ++++++++++++++++++
.../linalg/opdsl/ops/core_named_ops.py | 40 +++++
mlir/test/Dialect/Linalg/roundtrip.mlir | 15 ++
3 files changed, 194 insertions(+)
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
index bf2f26de26e9ed..4e3ef937d7d48f 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
@@ -4024,6 +4024,145 @@ structured_op: !LinalgStructuredOpConfig
- !ScalarExpression
scalar_arg: K
--- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+ name: conv_3d_ncdhw_fcdhw_q
+ cpp_class_name: Conv3DNcdhwFcdhwQOp
+ doc: |-
+ Performs 3-D convolution with zero point offsets.
+
+ 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, s11, s12,
+ s13, s14] -> (s0, s1, s2 * s3 + s4 * s5, s6 * s7 + s8 * s9, s10 * s11 + s12
+ * s13)>
+ - !LinalgOperandDefConfig
+ name: K
+ kind: input_tensor
+ type_var: T2
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12,
+ s13, s14] -> (s14, s1, s4, s8, s12)>
+ - !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, s11, s12,
+ s13, s14] -> (s0, s14, s2, s6, s10)>
+ - !LinalgOperandDefConfig
+ name: strides
+ kind: index_attr
+ index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11,
+ s12, s13, s14] -> (s3, s7, s11)>
+ default_indices:
+ - 1
+ - 1
+ - 1
+ - !LinalgOperandDefConfig
+ name: dilations
+ kind: index_attr
+ index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11,
+ s12, s13, s14] -> (s5, s9, s13)>
+ default_indices:
+ - 1
+ - 1
+ - 1
+ indexing_maps: !LinalgIndexingMapsConfig
+ static_indexing_maps:
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8)[s0, s1, s2, s3, s4, s5, s6,
+ s7, s8, s9, s10, s11, s12, s13, s14] -> (d0, d8, d1 * s3 + d5 * s5, d2 * s7
+ + d6 * s9, d3 * s11 + d7 * s13)>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8)[s0, s1, s2, s3, s4, s5, s6,
+ s7, s8, s9, s10, s11, s12, s13, s14] -> (d4, d8, d5, d6, d7)>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8)[s0, s1, s2, s3, s4, s5, s6,
+ s7, s8, s9, s10, s11, s12, s13, s14] -> ()>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8)[s0, s1, s2, s3, s4, s5, s6,
+ s7, s8, s9, s10, s11, s12, s13, s14] -> ()>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8)[s0, s1, s2, s3, s4, s5, s6,
+ s7, s8, s9, s10, s11, s12, s13, s14] -> (d0, d4, d1, d2, d3)>
+ iterator_types:
+ - parallel
+ - parallel
+ - parallel
+ - parallel
+ - parallel
+ - reduction
+ - 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: depthwise_conv_1d_nwc_wc
cpp_class_name: DepthwiseConv1DNwcWcOp
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 b45fecd0ee1457..4c7efc8d808767 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
@@ -1126,6 +1126,46 @@ def conv_3d_ncdhw_fcdhw(
],
) * TypeFn.cast_signed(U, K[D.f, D.c, D.kd, D.kh, D.kw])
+ at linalg_structured_op
+def conv_3d_ncdhw_fcdhw_q(
+ I=TensorDef(
+ T1,
+ S.N,
+ S.C,
+ S.OD * S.SD + S.KD * S.DD,
+ S.OH * S.SH + S.KH * S.DH,
+ S.OW * S.SW + S.KW * S.DW,
+ ),
+ K=TensorDef(T2, S.F, S.C, S.KD, S.KH, S.KW),
+ IZp=ScalarDef(I32),
+ KZp=ScalarDef(I32),
+ O=TensorDef(U, S.N, S.F, S.OD, S.OH, S.OW, output=True),
+ strides=IndexAttrDef(S.SD, S.SH, S.SW, default=[1, 1, 1]),
+ dilations=IndexAttrDef(S.DD, S.DH, S.DW, default=[1, 1, 1]),
+):
+ """Performs 3-D convolution with zero point offsets.
+
+ 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.od, D.oh, D.ow, D.f, D.kd, D.kh, D.kw, D.c)
+ O[D.n, D.f, D.od, D.oh, D.ow] += (
+ TypeFn.cast_signed(
+ U,
+ I[
+ D.n,
+ D.c,
+ D.od * S.SD + D.kd * S.DD,
+ 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.kd, D.kh, D.kw])
+ - TypeFn.cast_signed(U, KZp)
+ )
@linalg_structured_op
def depthwise_conv_1d_nwc_wc(
diff --git a/mlir/test/Dialect/Linalg/roundtrip.mlir b/mlir/test/Dialect/Linalg/roundtrip.mlir
index 1b8969bd115595..6e5adf007f58d7 100644
--- a/mlir/test/Dialect/Linalg/roundtrip.mlir
+++ b/mlir/test/Dialect/Linalg/roundtrip.mlir
@@ -694,3 +694,18 @@ func.func @conv2d_channel_first_q_promote(%img: tensor<100x3x224x224xi8>, %filt:
// 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>
+
+// -----
+
+func.func @conv3d_channel_first_q(%img: tensor<1x27x49x48x47xi8>, %filt: tensor<28x27x3x4x5xi8>, %a: i32, %b: i32) -> tensor<1x28x47x45x43xi32> {
+ %init = arith.constant dense<0> : tensor<1x28x47x45x43xi32>
+ %1 = linalg.conv_3d_ncdhw_fcdhw_q {dilations = dense<1> : tensor<3xi64>,
+ strides = dense<1> : tensor<3xi64>}
+ ins(%img, %filt, %a, %b : tensor<1x27x49x48x47xi8>, tensor<28x27x3x4x5xi8>, i32, i32)
+ outs(%init : tensor<1x28x47x45x43xi32>) -> tensor<1x28x47x45x43xi32>
+ return %1 : tensor<1x28x47x45x43xi32>
+}
+
+// CHECK-LABEL: func @conv3d_channel_first_q(
+// CHECK: %[[arg0:[a-zA-z0-9]*]]: tensor<1x27x49x48x47xi8>, %[[arg1:[a-zA-z0-9]*]]: tensor<28x27x3x4x5xi8>, %[[arg2:[a-zA-z0-9]*]]: i32, %[[arg3:[a-zA-z0-9]*]]: i32)
+// CHECK: linalg.conv_3d_ncdhw_fcdhw_q {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>} ins(%[[arg0]], %[[arg1]], %[[arg2]], %[[arg3]] : tensor<1x27x49x48x47xi8>, tensor<28x27x3x4x5xi8>, i32, i32) outs(%{{.*}} : tensor<1x28x47x45x43xi32>) -> tensor<1x28x47x45x43xi32>
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