LiBai MT5 不支持的op

[CPFLAME]

Unsupported ops: Counter({'broadcast_matmul': 89, 'scalar_div': 4, 'gather': 4, 'elementwise_minimum': 3, 'fill_': 2, 'scalar_logical_less': 2, 'where': 2, 'scalar_logical_greater': 1})

[doombeaker]

我正在拿这里的需求练手，为防止和其它人的工作重复了，我把已经开 PR 的列在这里：

• where, gather, broadcast_matmul, scalar_logical_less, scalar_logical_greater add op mapping for T5 #93
• scalar_div support scalar_div #84
• elementwise_minimum add elementwise max/min conversation and test case #92
• fill_ add mapping for "fill_" kernel #96

[daquexian]

fill_ 是 inplace 算子吗，在 job 层是怎么表示的呢

[doombeaker]

fill_ 是 inplace 算子吗

看了下，是 inplace 算子

在 job 层是怎么表示的呢

这个问题我还不懂…… 有请其它路过的人帮忙参与下

[BBuf]

输出一下job看一下吧。

[doombeaker]

输出一下job看一下吧。

我搭了个简单例子：

import tempfile
import oneflow as flow
from oneflow_onnx.oneflow2onnx.util import convert_to_onnx_and_check


class MathOps(flow.nn.Module):
    def __init__(self) -> None:
        super(MathOps, self).__init__()

    def forward(self, x: flow.Tensor) -> flow.Tensor:
        x.fill_(10.0)

        return x


math_ops = MathOps()


class MathOpGraph(flow.nn.Graph):
    def __init__(self):
        super().__init__()
        self.m = math_ops

    def build(self, x):
        out = self.m(x)
        return out


def test_math_ops():

    math_ops_graph = MathOpGraph()
    math_ops_graph._compile(flow.randn(1, 3, 224, 224))

    convert_to_onnx_and_check(math_ops_graph, onnx_model_path="/tmp")


test_math_ops()

输出的 job：

net {
  op {
    name: "_MathOpGraph_0_input.0.0_2"
    device_tag: "cpu"
    scope_symbol_id: 12
    input_conf {
      out: "out"
      blob_conf {
        shape {
          dim: 1
          dim: 3
          dim: 224
          dim: 224
        }
        data_type: kFloat
        is_dynamic: false
        nd_sbp {
          sbp_parallel {
            broadcast_parallel {
            }
          }
        }
      }
    }
  }
  op {
    name: "m-fill_-0"
    device_tag: "cpu"
    scope_symbol_id: 16
    loc: "Python Stack[-2]: \'build\' at \'onnx_mapping.py\': line 40; Python Stack[-1]: \'forward\' at \'onnx_mapping.py\': line 26;  ... more"
    user_conf {
      op_type_name: "fill_"
      input {
        key: "in"
        value {
          s: "_MathOpGraph_0_input.0.0_2/out"
        }
      }
      output {
        key: "out"
        value {
          s: "m-fill_-0/out_0"
        }
      }
      attr {
        key: "floating_value"
        value {
          at_double: 10.0
        }
      }
      attr {
        key: "integral_value"
        value {
          at_int64: 0
        }
      }
      attr {
        key: "is_floating_value"
        value {
          at_bool: true
        }
      }
      input_order: "in"
      output_order: "out"
    }
  }
  op {
    name: "_MathOpGraph_0_output.0.0_2"
    device_tag: "cpu"
    scope_symbol_id: 12
    output_conf {
      in: "m-fill_-0/out_0"
      out: "out"
      blob_conf {
        shape {
          dim: 1
          dim: 3
          dim: 224
          dim: 224
        }
        data_type: kFloat
        is_dynamic: false
        nd_sbp {
          sbp_parallel {
            broadcast_parallel {
            }
          }
        }
      }
    }
  }
}
placement {
  placement_group {
    op_set {
      op_name: "_MathOpGraph_0_input.0.0_2"
      op_name: "m-fill_-0"
      op_name: "_MathOpGraph_0_output.0.0_2"
    }
    parallel_conf {
      device_name: "@0:0"
      device_tag: "cpu"
      hierarchy {
        dim: 1
      }
    }
  }
  blob_placement_group {
    lbi {
      op_name: "_MathOpGraph_0_input.0.0_2"
      blob_name: "out"
    }
    lbi {
      op_name: "m-fill_-0"
      blob_name: "out_0"
    }
    lbi {
      op_name: "_MathOpGraph_0_output.0.0_2"
      blob_name: "out"
    }
    parallel_conf {
      device_name: "@0:0"
      device_tag: "cpu"
      hierarchy {
        dim: 1
      }
    }
  }
}
job_conf {
  job_name: "MathOpGraph_0"
  predict_conf {
  }
}
job_parallel_view_conf {
  op_name2sbp_signature_conf {
    key: "_MathOpGraph_0_input.0.0_2"
    value {
      bn_in_op2sbp_parallel {
        key: "out"
        value {
          broadcast_parallel {
          }
        }
      }
    }
  }
  op_name2sbp_signature_conf {
    key: "_MathOpGraph_0_output.0.0_2"
    value {
      bn_in_op2sbp_parallel {
        key: "in"
        value {
          broadcast_parallel {
          }
        }
      }
      bn_in_op2sbp_parallel {
        key: "out"
        value {
          broadcast_parallel {
          }
        }
      }
    }
  }
  op_name2sbp_signature_conf {
    key: "m-fill_-0"
    value {
      bn_in_op2sbp_parallel {
        key: "in_0"
        value {
          broadcast_parallel {
          }
        }
      }
      bn_in_op2sbp_parallel {
        key: "out_0"
        value {
          broadcast_parallel {
          }
        }
      }
    }
  }
  op_name2nd_sbp_signature_conf {
    key: "_MathOpGraph_0_input.0.0_2"
    value {
      bn_in_op2nd_sbp {
        key: "out"
        value {
          sbp_parallel {
            broadcast_parallel {
            }
          }
        }
      }
    }
  }
  op_name2nd_sbp_signature_conf {
    key: "_MathOpGraph_0_output.0.0_2"
    value {
      bn_in_op2nd_sbp {
        key: "in"
        value {
          sbp_parallel {
            broadcast_parallel {
            }
          }
        }
      }
      bn_in_op2nd_sbp {
        key: "out"
        value {
          sbp_parallel {
            broadcast_parallel {
            }
          }
        }
      }
    }
  }
  op_name2nd_sbp_signature_conf {
    key: "m-fill_-0"
    value {
      bn_in_op2nd_sbp {
        key: "in_0"
        value {
          sbp_parallel {
            broadcast_parallel {
            }
          }
        }
      }
      bn_in_op2nd_sbp {
        key: "out_0"
        value {
          sbp_parallel {
            broadcast_parallel {
            }
          }
        }
      }
    }
  }
}
helper {
  lbn2logical_blob_desc {
    key: "_MathOpGraph_0_input.0.0_2/out"
    value {
      shape {
        dim: 1
        dim: 3
        dim: 224
        dim: 224
      }
      stride {
        dim: 150528
        dim: 50176
        dim: 224
        dim: 1
      }
      data_type: kFloat
      is_dynamic: false
    }
  }
  lbn2logical_blob_desc {
    key: "_MathOpGraph_0_output.0.0_2/out"
    value {
      shape {
        dim: 1
        dim: 3
        dim: 224
        dim: 224
      }
      stride {
        dim: 150528
        dim: 50176
        dim: 224
        dim: 1
      }
      data_type: kFloat
      is_dynamic: false
    }
  }
  lbn2logical_blob_desc {
    key: "m-fill_-0/out_0"
    value {
      shape {
        dim: 1
        dim: 3
        dim: 224
        dim: 224
      }
      stride {
        dim: 150528
        dim: 50176
        dim: 224
        dim: 1
      }
      data_type: kFloat
      is_dynamic: false
    }
  }
  op_name2arg_signature {
    key: "_MathOpGraph_0_input.0.0_2"
    value {
      bn_in_op2lbi {
        key: "out"
        value {
          op_name: "_MathOpGraph_0_input.0.0_2"
          blob_name: "out"
        }
      }
    }
  }
  op_name2arg_signature {
    key: "_MathOpGraph_0_output.0.0_2"
    value {
      bn_in_op2lbi {
        key: "in"
        value {
          op_name: "m-fill_-0"
          blob_name: "out_0"
        }
      }
      bn_in_op2lbi {
        key: "out"
        value {
          op_name: "_MathOpGraph_0_output.0.0_2"
          blob_name: "out"
        }
      }
    }
  }
  op_name2arg_signature {
    key: "m-fill_-0"
    value {
      bn_in_op2lbi {
        key: "in_0"
        value {
          op_name: "_MathOpGraph_0_input.0.0_2"
          blob_name: "out"
        }
      }
      bn_in_op2lbi {
        key: "out_0"
        value {
          op_name: "m-fill_-0"
          blob_name: "out_0"
        }
      }
    }
  }
}
module_name2module_conf {
  key: "MathOpGraph_0"
  value {
    name: "MathOpGraph_0"
    ops: "_MathOpGraph_0_input.0.0_2"
    ops: "_MathOpGraph_0_output.0.0_2"
  }
}
module_name2module_conf {
  key: "m"
  value {
    name: "m"
    ops: "m-fill_-0"
  }
}

[BBuf]

@CPFLAME t5的op都支持了