如何添加一个Top层算子的Rewrite Pattern?

Viewed 1255
developer

本问题主要以Top.BatchNorm转为Top.Scale为例，展示如何添加一个Top层算子的Rewrite Pattern。

Charles1
1 Answers

一. 声明

在tpu-mlir/include/tpu_mlir/Dialect/Top/IR/TopOps.td文件中定义Top.BatchNorm算子时设置"let hasCanonicalizer = 1"使TableGen生成BatchNormOp::getCanonicalizationPatterns方法的声明。

def Top_BatchNormOp: Top_Op<"BatchNorm", [SupportFuseRelu]> {
  let summary = "BatchNormalization operation";
  let description = [{
     ...
  }];
  let arguments = (ins
    AnyTensor:$input,
    AnyTensor:$mean,
    AnyTensor:$variance,
    AnyTensorOrNone:$gamma,
    AnyTensorOrNone:$beta,
    DefaultValuedAttr<F64Attr, "1e-05">:$epsilon,
    DefaultValuedAttr<BoolAttr, "false">:$do_relu,
    DefaultValuedAttr<F64Attr, "-1.0">:$relu_limit
  );
  let results = (outs AnyTensor:$output);
  let hasCanonicalizer = 1;
}

二. 实现

在 tpu-mlir/lib/Dialect/Top/Canonicalize 路径下创建与算子同名的cpp文件BatchNorm.cpp。该文件中主要用于实现：

1. 针对该算子的OpRewritePattern结构体

  • 结构体由OpRewritePattern派生，需要使用OpRewritePattern::OpRewritePattern命名空间；
  • 需要实现matchAndRewrite方法，输入值是BatchNorm算子以及一个PatternRewriter，返回值是LogicalResult {success(), failure()}，前者表示匹配成功，执行重写操作，后者则为失败，退出当前Pattern并且不会对算子进行任何更改。
struct TopBatchNormToScale : public OpRewritePattern<BatchNormOp> {
  using OpRewritePattern::OpRewritePattern; 

  LogicalResult matchAndRewrite(BatchNormOp op, PatternRewriter &rewriter) 
					const override {return success();}
}

  • 通过TopOps.td 可以查看到一个标准的Top.Scale算子的输入除了Input外还有Scale与Bias Tensor，因此我们首先需要获取BatchNorm中的mean，variance，gamma和beta，并将其按以下公式计算转换为scale与bias的值
    image.png

  • 之后基于scale与bias两个vector创建相应的WeightOp，再通过rewriter.replaceOpWithNewOp方法将当前的BatchNormOp替换为ScaleOp，该方法所需的参数主要有:

    1. 用于替换的算子;
    2. 新的Output Type;
    3. 新算子的输入Inputs。

完整的matchAndRewrite代码如下：

  LogicalResult matchAndRewrite(BatchNormOp op,
                                PatternRewriter &rewriter) const override {

    auto mean = cast<WeightOp>(op.getMean().getDefiningOp());
    auto variance = cast<WeightOp>(op.getVariance().getDefiningOp());
    auto mean_f32 = mean.read<float>();
    auto variance_f32 = variance.read<float>();

    auto shape = module::getShape(op.getInput());
    auto channel = shape.size() > 1 ? shape[1] : shape[0];

    std::shared_ptr<std::vector<float>> gamma_f32;
    if (auto gamma = dyn_cast<WeightOp>(op.getGamma().getDefiningOp())) {
      gamma_f32 = gamma.read<float>();
    } else {
      gamma_f32 = std::make_shared<std::vector<float>>(channel, 1.0f);
    }
    std::shared_ptr<std::vector<float>> beta_f32;
    if (auto beta = dyn_cast<WeightOp>(op.getBeta().getDefiningOp())) {
      beta_f32 = beta.read<float>();
    } else {
      beta_f32 = std::make_shared<std::vector<float>>(channel, 0.0f);
    }

    std::vector<float> scale(channel);
    std::vector<float> bias(channel);

    // constructe scale and bias by params of BatchNorm
    auto eps = op.getEpsilon().convertToDouble();
    for (int i = 0; i < channel; ++i) {
      scale[i] = 1 / std::sqrt(variance_f32->at(i) + eps) * gamma_f32->at(i);
      bias[i] = -mean_f32->at(i) * scale[i] + beta_f32->at(i);
    }

    auto scale_type = RankedTensorType::get({channel}, rewriter.getF32Type());
    auto scale_op = WeightOp::create(op, "scale", scale, scale_type);
    auto bias_type = RankedTensorType::get({channel}, rewriter.getF32Type());
    auto bias_op = WeightOp::create(op, "bias", bias, bias_type);
    
	// replace the BatchNorm Op
    rewriter.replaceOpWithNewOp<ScaleOp>(
        op, op.getOutput().getType(),
        ValueRange{op.getInput(), scale_op, bias_op});
    return success();
  }

2. getCanonicalizationPatterns

  • 用于将上一步中实现的Pattern添加到PatternSet中，若存在多个Pattern则以 “,” 隔开。这些Pattern会在生成Top层mlir时被应用到转换过程中。
void BatchNormOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                              MLIRContext *context) {
  results.insert<TopBatchNormToScale>(context);
}

三. 测试

通过创建一个包含BatchNorm算子的onnx模型进行模型转换，对比*_origin.mlir和*.mlir文件中是否实现了从BatchNorm算子到Scale算子的转换。创建onnx算子时请参照ONNX算子官方文档
1. 在test_onnx.py中注册单元测试

class ONNX_IR_TESTER(object):
    # This class is built for testing single operator transform.
    def __init__(self, chip: str = "bm1684x", mode: str = "all", dynamic: bool = True):
        Y, N = True, False
        # yapf: disable
        self.test_cases = {
            #########################################
            # ONNX Test Case, Alphabetically
            #########################################
            # case: (test, bm1684x_support, bm1686_support, cv183x_support)
            "BN2Scale":          (self.test_BN2Scale,         Y, N, N),

2. 创建测试样例

    def test_BN2Scale(self, case_name):
        input_shape = [1, 65, 100, 100]
        output_shape = [1, 65, 100, 100]
        weight_shape = [65]
        gamma_data = np.random.randn(*weight_shape).astype(np.float32)
        beta_data = np.random.randn(*weight_shape).astype(np.float32)
        mean_data = np.random.randn(*weight_shape).astype(np.float32)
        var_data = np.random.randn(*weight_shape).astype(np.float32)

        input = helper.make_tensor_value_info("input", TensorProto.FLOAT, input_shape)
        output = helper.make_tensor_value_info("output", TensorProto.FLOAT, output_shape)
        gamma = helper.make_tensor('gamma', TensorProto.FLOAT, weight_shape, gamma_data)
        beta = helper.make_tensor('beta', TensorProto.FLOAT, weight_shape, beta_data)
        mean = helper.make_tensor('mean', TensorProto.FLOAT, weight_shape, mean_data)
        var = helper.make_tensor('var', TensorProto.FLOAT, weight_shape, var_data)


        bn_def = helper.make_node("BatchNormalization", inputs=["input", "gamma", "beta", "mean", "var"], outputs=["output"])

        graph_def = helper.make_graph([bn_def],
                                      case_name, [input], [output],
                                      initializer=[gamma, beta, mean, var])

        self.onnx_and_test(graph_def)

3. 在命令行中运行测试
test_onnx.py --case BN2Scale

4. 查看结果
对比BN2Scale_origin.mlir与BN2Scale.mlir的结果，可以观察到BatchNorm算子确实被转换为Scale算子。

BN2Scale_origin.mlir

#loc = loc(unknown)
module attributes {module.chip = "ALL", module.name = "BN2Scale", module.state = "TOP_F32", module.weight_file = "bn2scale_top_f32_all_origin_weight.npz"} {
  func.func @main(%arg0: tensor<1x65x100x100xf32> loc(unknown)) -> tensor<1x65x100x100xf32> {
    %0 = "top.None"() : () -> none loc(#loc)
    %1 = "top.Input"(%arg0) : (tensor<1x65x100x100xf32>) -> tensor<1x65x100x100xf32> loc(#loc1)
    %2 = "top.Weight"() : () -> tensor<65xf32> loc(#loc2)
    %3 = "top.Weight"() : () -> tensor<65xf32> loc(#loc3)
    %4 = "top.Weight"() : () -> tensor<65xf32> loc(#loc4)
    %5 = "top.Weight"() : () -> tensor<65xf32> loc(#loc5)
    %6 = "top.BatchNorm"(%1, %4, %5, %2, %3) {epsilon = 1.000000e-05 : f64} : (tensor<1x65x100x100xf32>, tensor<65xf32>, tensor<65xf32>, tensor<65xf32>, tensor<65xf32>) -> tensor<1x65x100x100xf32> loc(#loc6)
    return %6 : tensor<1x65x100x100xf32> loc(#loc)
  } loc(#loc)
} loc(#loc)
#loc1 = loc("input")
#loc2 = loc("gamma")
#loc3 = loc("beta")
#loc4 = loc("mean")
#loc5 = loc("var")
#loc6 = loc("output_BatchNormalization")

BN2Scale.mlir

#loc = loc(unknown)
module attributes {module.FLOPs = 650000 : i64, module.chip = "ALL", module.name = "BN2Scale", module.state = "TOP_F32", module.weight_file = "bn2scale_top_f32_all_weight.npz"} {
  func.func @main(%arg0: tensor<1x65x100x100xf32> loc(unknown)) -> tensor<1x65x100x100xf32> {
    %0 = "top.Input"(%arg0) : (tensor<1x65x100x100xf32>) -> tensor<1x65x100x100xf32> loc(#loc1)
    %1 = "top.Weight"() : () -> tensor<1x65xf32> loc(#loc2)
    %2 = "top.Weight"() : () -> tensor<1x65xf32> loc(#loc3)
    %3 = "top.Scale"(%0, %1, %2) {do_relu = false, relu_limit = -1.000000e+00 : f64} : (tensor<1x65x100x100xf32>, tensor<1x65xf32>, tensor<1x65xf32>) -> tensor<1x65x100x100xf32> loc(#loc4)
    return %3 : tensor<1x65x100x100xf32> loc(#loc)
  } loc(#loc)
} loc(#loc)
#loc1 = loc("input")
#loc2 = loc("output_BatchNormalization_scale")
#loc3 = loc("output_BatchNormalization_bias")
#loc4 = loc("output_BatchNormalization")

注意：实际应用过程中tpu-mlir/lib/Dialect/Top/Canonicalize/Scale.cpp中的Pattern还会将Scale算子进一步转换成DWConv算子，所以在添加Pattern的时候需要确定其是否与其它Pattern相关。

Charles1

rewrite pattern中返回值success和failure的问题可以参考问题https://ask.tpumlir.org/questions/10010000000000024/tian-jia-permutedao-zhi-rewrite-patternshi-zhi-xing-duo-ci-zhong-xie

zhang•