[Runtime][MatchShape] Type error: Cannot convert from type ' DLTensor* ' to ' ffi.Shape '

[locnd182644]

Summary

• This commit fixed error : "Type error: Cannot convert from type ' DLTensor* ' to ' ffi.Shape ' ".
• Error occurred at runtime when the first args of the MatchShape function (in runtime/vm/builtin.cc) is DLTensor*. If (auto opt_nd = args[0].as()) is false, args[0] (DLTensor*) will try convert to Shape.

Reproduce

RPC

• After over RPCWarppedFunc, Tensor will convert to DLTensor *

Cell In[3], line 189
    186 remote_input = tvm.runtime.tensor(input_data, dev)
    188 # Run inference on remote device
--> 189 output = vm["main"](remote_input)
    191 # Extract result (handle both tuple and single tensor outputs)
    192 if isinstance(output, tvm.ir.Array) and len(output) > 0:

File python/tvm_ffi/cython/function.pxi:904, in tvm_ffi.core.Function.__call__()

File ~/Programming/tvm/src/runtime/rpc/rpc_module.cc:141, in tvm::runtime::RPCWrappedFunc::operator()(tvm::ffi::PackedArgs, tvm::ffi::Any*) const()
    139   }
    140   auto set_return = [this, rv](ffi::PackedArgs args) { this->WrapRemoteReturnToValue(args, rv); };
--> 141   sess_->CallFunc(handle_, ffi::PackedArgs(packed_args.data(), packed_args.size()), set_return);
    142 }
    143 

File ~/Programming/tvm/src/runtime/rpc/rpc_endpoint.cc:1116, in tvm::runtime::RPCClientSession::CallFunc(void*, tvm::ffi::PackedArgs, std::function<void (tvm::ffi::PackedArgs)> const&)()
   1114 void CallFunc(PackedFuncHandle func, ffi::PackedArgs args,
   1115               const FEncodeReturn& fencode_return) final {
-> 1116   endpoint_->CallFunc(func, args, fencode_return);
   1117 }
   1118 

File ~/Programming/tvm/src/runtime/rpc/rpc_endpoint.cc:906, in tvm::runtime::RPCEndpoint::CallFunc(void*, tvm::ffi::PackedArgs, std::function<void (tvm::ffi::PackedArgs)>)()
    904   handler_->SendPackedSeq(args.data(), args.size(), true);
    905 
--> 906   code = HandleUntilReturnEvent(true, encode_return);
    907   ICHECK(code == RPCCode::kReturn) << "code=" << RPCCodeToString(code);
    908 }

File ~/Programming/tvm/src/runtime/rpc/rpc_endpoint.cc:746, in tvm::runtime::RPCEndpoint::HandleUntilReturnEvent(bool, std::function<void (tvm::ffi::PackedArgs)>)()
    744     }
    745   }
--> 746   code = handler_->HandleNextEvent(client_mode, false, setreturn);
    747 }
    748 return code;

File ~/Programming/tvm/src/runtime/rpc/rpc_endpoint.cc:134, in tvm::runtime::RPCEndpoint::EventHandler::HandleNextEvent(bool, bool, std::function<void (tvm::ffi::PackedArgs)>)()
    132 }
    133 case kProcessPacket: {
--> 134   this->HandleProcessPacket(setreturn);
    135   break;
    136 }

File ~/Programming/tvm/src/runtime/rpc/rpc_endpoint.cc:409, in tvm::runtime::RPCEndpoint::EventHandler::HandleProcessPacket(std::function<void (tvm::ffi::PackedArgs)>)()
    407 case RPCCode::kException:
    408 case RPCCode::kReturn: {
--> 409   this->HandleReturn(code, setreturn);
    410   break;
    411 }

File ~/Programming/tvm/src/runtime/rpc/rpc_endpoint.cc:473, in tvm::runtime::RPCEndpoint::EventHandler::HandleReturn(tvm::runtime::RPCCode, std::function<void (tvm::ffi::PackedArgs)>)()
    471     msg = "RPCError: Error caught from RPC call:\n" + msg;
    472   }
--> 473   LOG(FATAL) << msg;
    474 }
    475 

File ~/Programming/tvm/include/tvm/runtime/logging.h:321, in tvm::runtime::detail::LogFatal::~LogFatal()()
    319 #endif
    320   [[noreturn]] ~LogFatal() TVM_THROW_EXCEPTION {
--> 321     GetEntry().Finalize();
    322     throw;
    323   }

File ~/Programming/tvm/include/tvm/runtime/logging.h:337, in tvm::runtime::detail::LogFatal::Entry::Finalize()()
    335     }
    336     [[noreturn]] TVM_NO_INLINE dmlc::Error Finalize() TVM_THROW_EXCEPTION {
--> 337       InternalError error(file_, lineno_, stream_.str());
    338 #if DMLC_LOG_BEFORE_THROW
    339       std::cerr << error.what() << std::endl;

RPCError: Error caught from RPC call:
Cannot convert from type `DLTensor*` to `ffi.Shape`

C++

• Input is DLTensor *

std::cout << "--- TVM C++ TorchMLP Example ---" << std::endl;
int64_t shapes[] = {1, 1, 28, 28};
...
// Load binary data
std::vector<float> input_img = load_bin(argv[1], shapes[0] * shapes[1] * shapes[2] * shapes[3]);

// Create DLTensor for input
DLTensor* dl_tensor = new DLTensor();
dl_tensor->data = malloc(sizeof(float) * input_img.size());
dl_tensor->device = device;
dl_tensor->ndim = sizeof(shapes) / sizeof(shapes[0]);
dl_tensor->dtype = DLDataType{kDLFloat, 32, 1};
dl_tensor->shape = shapes;
dl_tensor->strides = nullptr;
dl_tensor->byte_offset = 0;

memcpy(dl_tensor->data, input_img.data(), sizeof(float) * input_img.size());

// Run the main function
tvm::ffi::Array<tvm::ffi::Tensor> ouput_temp = (*main)(dl_tensor).cast<tvm::ffi::Array<tvm::ffi::Tensor>>();

Resolved

• Instead of converting to Tensor, try_cast to DLTensor*, then create Shape

[gemini-code-assist]

Summary of Changes

Hello @locnd182644, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request addresses a critical runtime type conversion error within the MatchShape function, which previously led to failures when DLTensor* was passed as an argument. By enhancing the argument parsing to correctly interpret and process DLTensor* inputs, the change prevents crashes in RPC and C++ contexts. This improves the overall robustness and compatibility of the runtime system with various tensor representations, ensuring smoother execution of operations involving shape matching.

Highlights

• Fix MatchShape Type Error: Resolved a runtime type error in the MatchShape function where it failed to correctly convert a DLTensor* argument to ffi::Shape. This issue specifically occurred in scenarios involving RPC calls and direct C++ usage where DLTensor* is passed as the first argument.
• Improved Argument Handling: The MatchShape function's argument parsing logic has been updated to first attempt a try_cast to DLTensor*. If successful, it now constructs the ffi::Shape directly from the DLTensor*'s shape and ndim fields, ensuring proper handling of this tensor representation.

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[gemini-code-assist]

Code Review

This pull request addresses a runtime type error in MatchShape when it receives a DLTensor* argument. The fix correctly handles this case. However, my review identifies a critical regression where the support for tvm::runtime::Tensor arguments is unintentionally removed. I've provided a code suggestion to restore this functionality while keeping the fix, ensuring that MatchShape can correctly handle Tensor, DLTensor*, and ffi::Shape arguments as intended.

[gemini-code-assist]

The current change correctly adds support for DLTensor* arguments, which fixes the reported issue. However, it removes the existing support for tvm::runtime::Tensor arguments by replacing the check instead of adding to it. This will break existing code that passes a Tensor to MatchShape.

The logic should be updated to handle Tensor, DLTensor*, and ffi::Shape arguments, as indicated by the updated comment on line 125. You can chain the checks to support all three types.

  if (auto opt_tensor = args[0].as<Tensor>()) {
    input_shape = opt_tensor.value().Shape();
  } else if (auto opt_dltensor = args[0].try_cast<DLTensor*>()) {
    DLTensor* ptr = opt_dltensor.value();
    input_shape = ffi::Shape(ptr->shape, ptr->shape + ptr->ndim);
  } else {
    input_shape = args[0].cast<ffi::Shape>();
  }

[tlopex]

I think this review makes sense. Could you update the code like this?

[locnd182644]

I replaced Tensor to DLTensor* because I think tensor can cast to DLTensor*. This review makes sense because "as" operator cost is less than that of "cast" operator or other reason, could you tell me know.

[tlopex]

The main reason here is not related to the performance difference between as<Tensor>() and try_cast<DLTensor*>(). The issue is type coverage and backward compatibility. Tensor cannot be assumed to always cast safely to DLTensor*. Also, existing callers still pass tvm::runtime::Tensor as an argument. If we replace the Tensor branch with DLTensor*, the previous behavior may break.

[locnd182644]

I understand. I will update the code like this review

[locnd182644]

@tlopex Thank you for explaining. I updated the code.

[cbalint13]

@tvm-bot rerun

[tlopex]

I think this review makes sense. Could you update the code like this?

[tlopex]

Hi @locnd182644 This pr is the same. Could you retrigger the CI? Thanks!

[locnd182644]

@tlopex This PR has completed the check

[tlopex]

LGTM! Thank you!