How to parse CLIP to HAR?

[jayong-sv]

I noticed that CLIP is already present in the Hailo Model Zoo, which suggests that conversion is possible. link

I need help converting a model I trained myself. How can I parse CLIP to HAR? After converting a ResNet-based CLIP model to ONNX, I encountered the following error when parsing torch.nn.functional.multi_head_attention_forward from ONNX to HAR.

/local/workspace/hailo_virtualenv/bin/python /local/shared_with_docker/pycharm_codes/hailo_model_zoo/tutorials/clip-reid_parsing.py 
Model has been exported to attention_pool2d.onnx
[info] Translation started on ONNX model mha_test
[info] Restored ONNX model mha_test (completion time: 00:00:00.13)
[info] Extracted ONNXRuntime meta-data for Hailo model (completion time: 00:00:00.14)
[info] Attempting to retry parsing on a simplified model, using onnx simplifier
[info] Simplified ONNX model for a retry attempt (completion time: 00:00:01.65)
Traceback (most recent call last):
  File "/local/workspace/hailo_virtualenv/lib/python3.8/site-packages/hailo_sdk_client/sdk_backend/parser/parser.py", line 176, in translate_onnx_model
    parsing_results = self._parse_onnx_model_to_hn(onnx_model, valid_net_name, start_node_names,
  File "/local/workspace/hailo_virtualenv/lib/python3.8/site-packages/hailo_sdk_client/sdk_backend/parser/parser.py", line 231, in _parse_onnx_model_to_hn
    return self.parse_model_to_hn(onnx_model, None, net_name, start_node_names, end_node_names,
  File "/local/workspace/hailo_virtualenv/lib/python3.8/site-packages/hailo_sdk_client/sdk_backend/parser/parser.py", line 257, in parse_model_to_hn
    fuser = HailoNNFuser(converter.convert_model(), net_name, converter.end_node_names)
  File "/local/workspace/hailo_virtualenv/lib/python3.8/site-packages/hailo_sdk_client/model_translator/translator.py", line 63, in convert_model
    self._create_layers()
  File "/local/workspace/hailo_virtualenv/lib/python3.8/site-packages/hailo_sdk_client/model_translator/edge_nn_translator.py", line 26, in _create_layers
    self._add_direct_layers()
  File "/local/workspace/hailo_virtualenv/lib/python3.8/site-packages/hailo_sdk_client/model_translator/edge_nn_translator.py", line 131, in _add_direct_layers
    raise ParsingWithRecommendationException(
hailo_sdk_client.model_translator.exceptions.ParsingWithRecommendationException: Parsing failed. The errors found in the graph are:
 UnsupportedShuffleLayerError in op /Reshape_2: Unable to create shuffle layer at /Reshape_2
 UnsupportedShuffleLayerError in op /Reshape_3: Unable to create shuffle layer at /Reshape_3
 UnsupportedShuffleLayerError in op /Reshape_1: Unable to create shuffle layer at /Reshape_1
 UnsupportedShuffleLayerError in op /Reshape_5: Failed to determine type of layer to create in node /Reshape_5
 UnsupportedShuffleLayerError in op /Reshape_6: Failed to determine type of layer to create in node /Reshape_6
 UnsupportedShuffleLayerError in op /Reshape_4: Failed to determine type of layer to create in node /Reshape_4
 UnsupportedShuffleLayerError in op /Transpose_5: Failed to determine type of layer to create in node /Transpose_5
 UnsupportedShuffleLayerError in op /Reshape_7: Failed to determine type of layer to create in node /Reshape_7
Please try to parse the model again, using these end node names: /Add_3, /Add_2, /Constant_19, /Constant_22, /Constant_20, /Constant_15, /Add_1

Reproduction Code

The code to reproduce the error is as follows. AttentionPool2d is taken from the OpenAI CLIP code: link

from hailo_sdk_client import ClientRunner
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.onnx

class AttentionPool2d(nn.Module):
    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
        super().__init__()
        self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
        self.k_proj = nn.Linear(embed_dim, embed_dim)
        self.q_proj = nn.Linear(embed_dim, embed_dim)
        self.v_proj = nn.Linear(embed_dim, embed_dim)
        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
        self.num_heads = num_heads

    def forward(self, x):
        x = x.flatten(start_dim=2).permute(2, 0, 1)  # NCHW -> (HW)NC
        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
        x, _ = F.multi_head_attention_forward(
            query=x[:1], key=x, value=x,
            embed_dim_to_check=x.shape[-1],
            num_heads=self.num_heads,
            q_proj_weight=self.q_proj.weight,
            k_proj_weight=self.k_proj.weight,
            v_proj_weight=self.v_proj.weight,
            in_proj_weight=None,
            in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
            bias_k=None,
            bias_v=None,
            add_zero_attn=False,
            dropout_p=0,
            out_proj_weight=self.c_proj.weight,
            out_proj_bias=self.c_proj.bias,
            use_separate_proj_weight=True,
            training=self.training,
            need_weights=False
        )
        return x.squeeze(0)

# Model definition
spacial_dim = 7  # Adjusted to match the input data size of 7x7
embed_dim = 2048
num_heads = 8
output_dim = 1024
model = AttentionPool2d(spacial_dim, embed_dim, num_heads, output_dim)

# Generate dummy input (batch_size=1, channels=2048, height=7, width=7)
dummy_input = torch.randn(1, embed_dim, 7, 7)

# Export the model to ONNX format
onnx_path = "attention_pool2d.onnx"
model_name = "attention_pool2d"
torch.onnx.export(model, dummy_input, onnx_path,
                  export_params=True, opset_version=17,
                  do_constant_folding=True, input_names=['input'],
                  output_names=['output'],
                  dynamic_axes={'input': {0: 'batch_size'}, 'output': {0: 'batch_size'}})

print(f"Model has been exported to {onnx_path}")

runner = ClientRunner(hw_arch='hailo8')
hn, npz = runner.translate_onnx_model(onnx_path, 'mha_test', net_input_shapes={'input': list(dummy_input.shape)})

Execution Environment

• HailoRT v4.16.0
• Hailo Dataflow Compiler v3.26.0

---

[omerwer]

Hi @jayong-sv, In the version you are working with, the Clip model is not supported. In the current release (DFC 3.28.0), only the clip_resnet image encoder is supported. More Clip models, including the text encoder, would be supported in the future.

Regards,

[jayong-sv]

@omerwer Hello, The error message I posted was occurred when I converted clip_resnet image encoder. F.multi_head_attention_forward is included in clip_resnet.