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ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
https://onnxruntime.ai
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[Mobile] QNN failed to finalize QNN graph for attention layer #21221

Open Novelfor opened 2 months ago

Novelfor commented 2 months ago

Describe the issue

I train a qat self attention model by Pytorch FX, the model can be run in libQnnCpu.so but error in libQnnHtp.so. The model run in linux x86. QNN: 2.20.0.240223 ERROR Message:

2024-07-01 21:56:45.190463253 [V:onnxruntime:test, qnn_backend_manager.cc:249 QnnLogging] graph_prepare.cc:205:ERROR:could not create op: q::flat_from_vtcm

2024-07-01 21:56:45.190499590 [V:onnxruntime:test, qnn_backend_manager.cc:249 QnnLogging] graph_prepare.cc:1168:ERROR:Op 0x1aa00000005b preparation failed with err:-1

2024-07-01 21:56:45.190521298 [V:onnxruntime:test, qnn_backend_manager.cc:249 QnnLogging] QnnDsp <E> "/MatMul" generated: could not create op

2024-07-01 21:56:45.190525810 [V:onnxruntime:test, qnn_backend_manager.cc:249 QnnLogging] QnnDsp <E> RouterX86 graph prepare failed 12

2024-07-01 21:56:45.190532281 [V:onnxruntime:test, qnn_backend_manager.cc:249 QnnLogging] QnnDsp <E> Failed to finalize graph (id: 1) with err 1002

2024-07-01 21:56:45.190539347 [V:onnxruntime:test, qnn_backend_manager.cc:249 QnnLogging] QnnDsp <V> Wake up free backend 1 thread(s)

2024-07-01 21:56:45.190549138 [V:onnxruntime:test, qnn_backend_manager.cc:249 QnnLogging] QnnDsp <I> QnnGraph_finalize done. status 0x3ea

2024-07-01 21:56:45.190555828 [E:onnxruntime:, qnn_model.cc:181 FinalizeGraphs] Failed to finalize QNN graph.

To reproduce

I write the minimal reproduce code, the pytorch code to generate "test_int8.onnx", and use c++ code to run it.

I only test it in linux x86, but i guess it will be consistent on the Android side.

import torch
import torch.nn as nn
import torch.nn.functional as F

class Attention(nn.Module):
    def __init__(
            self,
            dim,
            num_heads=8,
            qkv_bias=False,
            qk_norm=False,
            attn_drop=0.,
            proj_drop=0.,
            norm_layer=nn.LayerNorm,
    ):
        super().__init__()
        assert dim % num_heads == 0, f'dim {dim} should be divisible by num_heads'
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.scale = self.head_dim ** -0.5
        self.fused_attn = True

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
        self.attn_mask = None

    def forward(self, x):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads,
                                  self.head_dim).permute(2, 0, 3, 1, 4)
        q, k, v = qkv.unbind(0)
        q, k = self.q_norm(q), self.k_norm(k)

        x = F.scaled_dot_product_attention(
            q, k, v,
            dropout_p=self.attn_drop.p if self.training else 0.,
        )

        x = x.transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

def to_static_shape_onnx(model_file):
    mp = onnx.load(model_file)
    mp.graph.input[0].type.tensor_type.shape.dim[0].dim_value = 1    
    mp.graph.output[0].type.tensor_type.shape.dim[0].dim_value = 1    
    mp = onnx.shape_inference.infer_shapes(mp)
    onnx.save(mp, model_file)

from torch.ao.quantization import (
    get_default_qconfig,
    get_default_qat_qconfig,
    QConfigMapping,
)
import torch.ao.quantization.quantize_fx as quantize_fx

attn_model = Attention(320, 10)
attn_model(torch.randn(1, 10, 320))

qconfig_mapping = QConfigMapping()\
    .set_global(get_default_qat_qconfig("qnnpack"))

model_prepared = quantize_fx.prepare_qat_fx(
    attn_model, qconfig_mapping, (torch.randn(1, 10, 320), ))

for i in range(10):
    model_prepared(torch.randn(1, 10, 320))

quant_model = quantize_fx.convert_fx(model_prepared)
torch.onnx.export(quant_model, torch.randn(1, 10, 320), "test_int8.onnx", input_names=['input'], output_names=['output'])
to_static_shape_onnx("test_int8.onnx")

Init this model in c++

#include <iostream>
#include <vector>
#include "session/onnxruntime_cxx_api.h"

template <class T>
inline std::vector<float> forward(Ort::Session& session, std::vector<T>& inputs, 
    const std::vector<int64_t>& input_shape_, const std::vector<const char*> input_names, const std::vector<const char*> output_names) {
    std::vector<float> temp_data;
    temp_data.resize(inputs.size());
    for (int i = 0; i < inputs.size(); i++) {
        temp_data[i] = inputs[i];
    }

    auto memory_info = Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU);
    auto input_tensor_ = Ort::Value::CreateTensor<float>(memory_info, temp_data.data(), temp_data.size(), input_shape_.data(), input_shape_.size());
    float* input_buffer = input_tensor_.GetTensorMutableData<float>();
    Ort::Value output_tensor_(nullptr);
    session.Run(Ort::RunOptions{nullptr}, input_names.data(), &input_tensor_, input_names.size(), output_names.data(), &output_tensor_, output_names.size());
    float* output_buffer = output_tensor_.GetTensorMutableData<float>();
    std::vector<float> output_data(output_buffer, output_buffer + output_tensor_.GetTensorTypeAndShapeInfo().GetElementCount());
    return output_data;
}

bool CheckStatus(const OrtApi* g_ort, OrtStatus* status) {
  if (status != nullptr) {
    const char* msg = g_ort->GetErrorMessage(status);
    std::cerr << msg << std::endl;
    g_ort->ReleaseStatus(status);
    throw Ort::Exception(msg, OrtErrorCode::ORT_EP_FAIL);
  }
  return true;
}

int main() {
    const OrtApi* g_ort = OrtGetApiBase()->GetApi(ORT_API_VERSION);
    OrtEnv* env;
    // g_ort->CreateEnv(ORT_LOGGING_LEVEL_VERBOSE, "test", &env);
    g_ort->CreateEnv(ORT_LOGGING_LEVEL_VERBOSE, "test", &env);

    OrtSessionOptions* session_options;
    CheckStatus(g_ort, g_ort->CreateSessionOptions(&session_options));
    CheckStatus(g_ort, g_ort->SetIntraOpNumThreads(session_options, 1));
    CheckStatus(g_ort, g_ort->SetSessionGraphOptimizationLevel(session_options, ORT_ENABLE_BASIC));

    std::vector<const char*> options_keys = {"backend_path"};
    std::vector<const char*> options_values = {"libQnnHtp.so"};

    CheckStatus(g_ort, g_ort->SessionOptionsAppendExecutionProvider(session_options, "QNN", options_keys.data(), options_values.data(), options_keys.size()));

    OrtSession* session;
    CheckStatus(g_ort, g_ort->CreateSession(env, "test_int8.onnx", session_options, &session));

    std::cout << "FINISH" << std::endl;
    return 0;
}

Urgency

No response

Platform

Android

OS Version

linux

ONNX Runtime Installation

Built from Source

Compiler Version (if 'Built from Source')

gcc9

Package Name (if 'Released Package')

None

ONNX Runtime Version or Commit ID

8c2689877fb48bfb4a6a133b020cdb5ec7c9b066

ONNX Runtime API

C++/C

Architecture

X64

Execution Provider

Other / Unknown

Execution Provider Library Version

QNN: 2.20.0.240223

Unicorncosmos commented 1 month ago

Hi, @Novelfor did you found any rectification?.. for the qnn model run on the htp the same issue iam also facing runs on cpu not htp ` 0.0ms [ ERROR ] graph_prepare.cc:203:ERROR:could not create op: q::flat_from_vtcm

 0.0ms [ ERROR ] graph_prepare.cc:1187:ERROR:Op 0x1a47e400000026 preparation failed with err:-1

 0.0ms [ ERROR ] QnnDsp <E> "_encoder_backbone_backbone_0_Conv" generated: could not create op

 0.0ms [ ERROR ] QnnDsp <E> RouterX86 graph prepare failed 12

 0.0ms [ ERROR ] QnnDsp <E> Failed to finalize graph (id: 1) with err 1002

 0.0ms [VERBOSE] QnnDsp <V> Wake up free backend 1 thread(s)

 0.0ms [  INFO ] QnnDsp <I> QnnGraph_finalize done. status 0x3ea

43309.1ms [ ERROR ] Finalize Graph for Idx = 0 failed with error = 1002 Graph Finalize failure 0.0ms [VERBOSE] QnnDsp Final cleanup: free backend id 0x1

 0.0ms [WARNING] QnnDsp <W> Backend 1 free cleanup called during process exit

 0.0ms [VERBOSE] QnnDsp <V> Terminated backend 0x1 successfully in backendLifecycleManager

 0.0ms [VERBOSE] QnnDsp <V> Final context cleanup: contextId = 1!

 0.0ms [VERBOSE] QnnDsp <V> qnnOpPackageManager: unloading OpPackages...

 0.0ms [VERBOSE] QnnDsp <V> qnnOpPackageManager: OpPackge already unloaded.

 0.0ms [VERBOSE] QnnDsp <V> RouterNative tryUnLoadPrepare Disabled`
Novelfor commented 1 month ago

I try use aimet to quantize transformer... it works https://github.com/quic/aimet