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torch-mlir/test/Conversion/TorchToLinalg/convolution.mlir

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[MLIR][Torch] Do not convert bias tensor to element type if NoneType (#3072) The `convertTensorToElementType` function expects it's argument to have a valid tensor type that is not `Torch::NoneType`. This PR checks that the bias tensor is not of type `Torch::NoneType` before calling `convertTensorToElementType` on the bias tensor argument in the `matchAndRewrite` member function of the `ConvertAtenConvolutionOp` class.
2024-04-02 16:49:26 +08:00
// RUN: torch-mlir-opt <%s -convert-torch-to-linalg -canonicalize -split-input-file -mlir-print-local-scope -verify-diagnostics | FileCheck %s
// CHECK-LABEL: func @torch.aten.convolution$nobias(
// CHECK: %[[CONSTANT:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[FILL_RESULT:.*]] = linalg.fill ins(%[[CONSTANT]] : f32) outs(%{{.*}} : tensor<1x54x16x128x128xf32>) -> tensor<1x54x16x128x128xf32>
// CHECK: %[[CONV3D:.*]] = linalg.conv_3d_ncdhw_fcdhw {{.*}} outs(%[[FILL_RESULT]] : tensor<1x54x16x128x128xf32>) -> tensor<1x54x16x128x128xf32>
func.func @torch.aten.convolution$nobias(%arg0: !torch.vtensor<[1,24,16,128,128],f16>, %arg1: !torch.vtensor<[54,24,1,1,1],f16>) -> !torch.vtensor<[1,54,16,128,128],f16> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 19 : si64, torch.onnx_meta.producer_name = "", torch.onnx_meta.producer_version = ""} {
%none = torch.constant.none
%false = torch.constant.bool false
%int0 = torch.constant.int 0
%int1 = torch.constant.int 1
%0 = torch.prim.ListConstruct %int0, %int0, %int0 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%1 = torch.prim.ListConstruct %int1, %int1, %int1 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%2 = torch.prim.ListConstruct %int1, %int1, %int1 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%3 = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
%4 = torch.aten.convolution %arg0, %arg1, %none, %2, %0, %1, %false, %3, %int1 : !torch.vtensor<[1,24,16,128,128],f16>, !torch.vtensor<[54,24,1,1,1],f16>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,54,16,128,128],f16>
return %4 : !torch.vtensor<[1,54,16,128,128],f16>
}
[TorchToLinalg] Fix Quantized Convolution Accumulator Type (#3459) 1. truncates zero-points to i32 2. modifies the default accumulator type for i8 from i64 to i32. 3. now uses the input dtype to infer accumulator dtype.
2024-06-21 04:54:20 +08:00
// -----
// CHECK-LABEL: func.func @q_conv_test
// CHECK: %[[c3:.*]] = arith.constant 3 : i32
// CHECK: %[[c7:.*]] = arith.constant 7 : i32
// CHECK: %[[input:.*]] = torch_c.to_builtin_tensor %arg0 : !torch.vtensor<[?,?,?,?],si8> -> tensor<?x?x?x?xi8>
// CHECK: %[[weight:.*]] = torch_c.to_builtin_tensor %arg1 : !torch.vtensor<[?,?,?,?],si8> -> tensor<?x?x?x?xi8>
// CHECK: %[[TransInput:.*]] = linalg.transpose ins(%[[input]] : tensor<?x?x?x?xi8>)
// CHECK-SAME: permutation = [0, 2, 3, 1]
// CHECK: %[[TransWeight:.*]] = linalg.transpose ins(%[[weight]] : tensor<?x?x?x?xi8>)
// CHECK-SAME: permutation = [2, 3, 1, 0]
// CHECK: %[[conv:.*]] = linalg.conv_2d_nhwc_hwcf_q {dilations = dense<1> : vector<2xi64>, strides = dense<1> : vector<2xi64>}
// CHECK-SAME: ins(%[[TransInput]], %[[TransWeight]], %[[c7]], %[[c3]] : tensor<?x?x?x?xi8>, tensor<?x?x?x?xi8>, i32, i32)
// CHECK-SAME: outs(%[[convout:.*]] : tensor<?x?x?x?xi32>) -> tensor<?x?x?x?xi32>
func.func @q_conv_test(%arg0: !torch.vtensor<[?,?,?,?],si8>, %arg1: !torch.vtensor<[?,?,?,?],si8>, %arg2: !torch.vtensor<[?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
%false = torch.constant.bool false
%int1 = torch.constant.int 1
%int0 = torch.constant.int 0
%float1.000000e-04 = torch.constant.float 1.000000e-04
%int3 = torch.constant.int 3
%int7 = torch.constant.int 7
%float1.000000e-02 = torch.constant.float 1.000000e-02
%int14 = torch.constant.int 14
%0 = torch.aten.quantize_per_tensor %arg2, %float1.000000e-04, %int0, %int14 : !torch.vtensor<[?],f32>, !torch.float, !torch.int, !torch.int -> !torch.vtensor<[?],!torch.qint32>
%1 = torch.aten.dequantize.self %0 : !torch.vtensor<[?],!torch.qint32> -> !torch.vtensor<[?],f32>
%2 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
%3 = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
%4 = torch.prim.ListConstruct : () -> !torch.list<int>
%5 = torch.aten._make_per_tensor_quantized_tensor %arg0, %float1.000000e-02, %int7 : !torch.vtensor<[?,?,?,?],si8>, !torch.float, !torch.int -> !torch.vtensor<[?,?,?,?],!torch.qint8>
%6 = torch.aten._make_per_tensor_quantized_tensor %arg1, %float1.000000e-02, %int3 : !torch.vtensor<[?,?,?,?],si8>, !torch.float, !torch.int -> !torch.vtensor<[?,?,?,?],!torch.qint8>
%7 = torch.aten.quantize_per_tensor %1, %float1.000000e-04, %int0, %int14 : !torch.vtensor<[?],f32>, !torch.float, !torch.int, !torch.int -> !torch.vtensor<[?],!torch.qint32>
%8 = torch.aten.int_repr %7 : !torch.vtensor<[?],!torch.qint32> -> !torch.vtensor<[?],si32>
%9 = torch.aten.convolution %5, %6, %8, %2, %3, %2, %false, %4, %int1 : !torch.vtensor<[?,?,?,?],!torch.qint8>, !torch.vtensor<[?,?,?,?],!torch.qint8>, !torch.vtensor<[?],si32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[?,?,?,?],si32>
%10 = torch.aten._make_per_tensor_quantized_tensor %9, %float1.000000e-04, %int0 : !torch.vtensor<[?,?,?,?],si32>, !torch.float, !torch.int -> !torch.vtensor<[?,?,?,?],!torch.qint32>
%11 = torch.aten.dequantize.tensor %10 : !torch.vtensor<[?,?,?,?],!torch.qint32> -> !torch.vtensor<[?,?,?,?],f32>
return %11 : !torch.vtensor<[?,?,?,?],f32>
}
[TorchToLinalg][test] Add test for ConvertAtenConvolutionOp (#3679) This patch add a test for 638ef14, which use `linalg.broadcast` instead of `generic` for convolution bias. Co-authored-by: Rongsheng Gao <gaorongsheng@huawei.com>
2024-08-30 17:51:50 +08:00
// -----
// CHECK-LABEL: func.func @conv_broadcast(
// CHECK-SAME: %[[arg0:.*]]: !torch.vtensor<[1,80,3000],f32>,
// CHECK-SAME: %[[arg1:.*]]: !torch.vtensor<[1024,80,3],f32>,
// CHECK-SAME: %[[arg2:.*]]: !torch.vtensor<[1024],f32>) -> !torch.vtensor<[1,1024,3000],f32> {
// CHECK: %[[c0:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[input:.*]] = torch_c.to_builtin_tensor %[[arg0]] : !torch.vtensor<[1,80,3000],f32> -> tensor<1x80x3000xf32>
// CHECK-DAG: %[[weight:.*]] = torch_c.to_builtin_tensor %[[arg1]] : !torch.vtensor<[1024,80,3],f32> -> tensor<1024x80x3xf32>
// CHECK-DAG: %[[bias:.*]] = torch_c.to_builtin_tensor %[[arg2]] : !torch.vtensor<[1024],f32> -> tensor<1024xf32>
// CHECK: %[[padInput:.*]] = tensor.pad %[[input]] low[0, 0, 1] high[0, 0, 1]
// CHECK: %[[EMPTY:.*]] = tensor.empty() : tensor<1x1024x3000xf32>
// CHECK: %[[broadcastBias:.*]] = linalg.broadcast ins(%[[bias]] : tensor<1024xf32>) outs(%[[EMPTY]] : tensor<1x1024x3000xf32>) dimensions = [0, 2]
// CHECK: %[[conv:.*]] = linalg.conv_1d_ncw_fcw {dilations = dense<1> : vector<1xi64>, strides = dense<1> : vector<1xi64>}
// CHECK-SAME: ins(%[[padInput:.*]], %[[weight]] : tensor<1x80x3002xf32>, tensor<1024x80x3xf32>)
// CHECK-SAME: outs(%[[broadcastBias]] : tensor<1x1024x3000xf32>) -> tensor<1x1024x3000xf32>
func.func @conv_broadcast(%arg0: !torch.vtensor<[1,80,3000],f32>, %arg1: !torch.vtensor<[1024,80,3],f32>, %arg2: !torch.vtensor<[1024],f32>) -> !torch.vtensor<[1,1024,3000],f32> {
%int0 = torch.constant.int 0
%int1 = torch.constant.int 1
%false = torch.constant.bool false
%0 = torch.prim.ListConstruct %int1 : (!torch.int) -> !torch.list<int>
%1 = torch.prim.ListConstruct %int0 : (!torch.int) -> !torch.list<int>
%2 = torch.aten.convolution %arg0, %arg1, %arg2, %0, %0, %0, %false, %1, %int1 : !torch.vtensor<[1,80,3000],f32>, !torch.vtensor<[1024,80,3],f32>, !torch.vtensor<[1024],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,1024,3000],f32>
return %2 : !torch.vtensor<[1,1024,3000],f32>
}