What is a correct configuration for TensorRT for my Speech-To-Text PyTorch model?

aram.faghfouri · January 3, 2023, 6:43pm

Description

I have a Speech-To-Text PyTorch model that I would like to convert to TensorRT.
The model structure is shown below in the “Relevant Files” section.
It has two inputs:

tokens: torch.Size([1, 1]) (Data Type: integer)
audio_features: torch.Size([1, 1, 512]) (Data Type: FP16 )
and one output:
out: torch.Size([1, 1, 51865]) (Data Type: FP16)

If I generate inputs with the above size and run the model, I get the expected output.

How can I set up the configuration so that I can convert it to a TensorRT model object that can be deployed using Triton?

A clear and concise description of the bug or issue.
I used the following code:

x = torch.Tensor([1,1]).to("cuda")
y = torch.Tensor([1,1,512]).to("cuda")

inputs= [
        torch_tensorrt.Input(x.shape),
        torch_tensorrt.Input(y.shape)
    ]

# Compile with Torch TensorRT;
trt_model = torch_tensorrt.compile(model,
    inputs=inputs,
    enabled_precisions= { torch.half } # Run with FP16
)

Here is the error I get ( The file a.py includes the above lines):

Traceback (most recent call last):
  File "a.py", line 47, in <module>
    trt_model = torch_tensorrt.compile(model,
  File "/usr/local/lib/python3.8/dist-packages/torch_tensorrt/_compile.py", line 124, in compile
    ts_mod = torch.jit.script(module)
  File "/usr/local/lib/python3.8/dist-packages/torch/jit/_script.py", line 1286, in script
    return torch.jit._recursive.create_script_module(
  File "/usr/local/lib/python3.8/dist-packages/torch/jit/_recursive.py", line 476, in create_script_module
    return create_script_module_impl(nn_module, concrete_type, stubs_fn)
  File "/usr/local/lib/python3.8/dist-packages/torch/jit/_recursive.py", line 538, in create_script_module_impl
    script_module = torch.jit.RecursiveScriptModule._construct(cpp_module, init_fn)
  File "/usr/local/lib/python3.8/dist-packages/torch/jit/_script.py", line 615, in _construct
    init_fn(script_module)
  File "/usr/local/lib/python3.8/dist-packages/torch/jit/_recursive.py", line 516, in init_fn
    scripted = create_script_module_impl(orig_value, sub_concrete_type, stubs_fn)
  File "/usr/local/lib/python3.8/dist-packages/torch/jit/_recursive.py", line 538, in create_script_module_impl
    script_module = torch.jit.RecursiveScriptModule._construct(cpp_module, init_fn)
  File "/usr/local/lib/python3.8/dist-packages/torch/jit/_script.py", line 615, in _construct
    init_fn(script_module)
  File "/usr/local/lib/python3.8/dist-packages/torch/jit/_recursive.py", line 516, in init_fn
    scripted = create_script_module_impl(orig_value, sub_concrete_type, stubs_fn)
  File "/usr/local/lib/python3.8/dist-packages/torch/jit/_recursive.py", line 542, in create_script_module_impl
    create_methods_and_properties_from_stubs(concrete_type, method_stubs, property_stubs)
  File "/usr/local/lib/python3.8/dist-packages/torch/jit/_recursive.py", line 393, in create_methods_and_properties_from_stubs
    concrete_type._create_methods_and_properties(property_defs, property_rcbs, method_defs, method_rcbs, method_defaults)
  File "/usr/local/lib/python3.8/dist-packages/torch/jit/_recursive.py", line 894, in compile_unbound_method
    create_methods_and_properties_from_stubs(concrete_type, (stub,), ())
  File "/usr/local/lib/python3.8/dist-packages/torch/jit/_recursive.py", line 393, in create_methods_and_properties_from_stubs
    concrete_type._create_methods_and_properties(property_defs, property_rcbs, method_defs, method_rcbs, method_defaults)
RuntimeError:

  File "/usr/local/lib/python3.8/dist-packages/STT/model.py", line 43
    def _conv_forward(self, x: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor:
        return super()._conv_forward(
               ~~~~~~~~~~~~~~~~~~~ <--- HERE
            x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype)
        )
'Conv1d._conv_forward' is being compiled since it was called from 'Conv1d.forward'
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py", line 313
    def forward(self, input: Tensor) -> Tensor:
        return self._conv_forward(input, self.weight, self.bias)
               ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE

Environment

TensorRT Version:
GPU Type:

Tesla M60

Nvidia Driver Version:

525

CUDA Version:

11.8

CUDNN Version:

8.7.0.84

Operating System + Version:

Ubuntu 20.04

Python Version (if applicable):
3.8
TensorFlow Version (if applicable):

N/A

PyTorch Version (if applicable):

1.13.0.post200

Baremetal or Container (if container which image + tag):

NA

Relevant Files

Here is the Speech-To_Text (STT) model structure:

STT(
  (encoder): AudioEncoder(
    (conv1): Conv1d(80, 512, kernel_size=(3,), stride=(1,), padding=(1,))
    (conv2): Conv1d(512, 512, kernel_size=(3,), stride=(2,), padding=(1,))
    (blocks): ModuleList(
      (0): ResidualAttentionBlock(
        (attn): MultiHeadAttention(
          (query): Linear(in_features=512, out_features=512, bias=True)
          (key): Linear(in_features=512, out_features=512, bias=False)
          (value): Linear(in_features=512, out_features=512, bias=True)
          (out): Linear(in_features=512, out_features=512, bias=True)
        )
        (attn_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (mlp): Sequential(
          (0): Linear(in_features=512, out_features=2048, bias=True)
          (1): GELU(approximate='none')
          (2): Linear(in_features=2048, out_features=512, bias=True)
        )
        (mlp_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
      (1): ResidualAttentionBlock(
        (attn): MultiHeadAttention(
          (query): Linear(in_features=512, out_features=512, bias=True)
          (key): Linear(in_features=512, out_features=512, bias=False)
          (value): Linear(in_features=512, out_features=512, bias=True)
          (out): Linear(in_features=512, out_features=512, bias=True)
        )
        (attn_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (mlp): Sequential(
          (0): Linear(in_features=512, out_features=2048, bias=True)
          (1): GELU(approximate='none')
          (2): Linear(in_features=2048, out_features=512, bias=True)
        )
        (mlp_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
      (2): ResidualAttentionBlock(
        (attn): MultiHeadAttention(
          (query): Linear(in_features=512, out_features=512, bias=True)
          (key): Linear(in_features=512, out_features=512, bias=False)
          (value): Linear(in_features=512, out_features=512, bias=True)
          (out): Linear(in_features=512, out_features=512, bias=True)
        )
        (attn_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (mlp): Sequential(
          (0): Linear(in_features=512, out_features=2048, bias=True)
          (1): GELU(approximate='none')
          (2): Linear(in_features=2048, out_features=512, bias=True)
        )
        (mlp_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
      (3): ResidualAttentionBlock(
        (attn): MultiHeadAttention(
          (query): Linear(in_features=512, out_features=512, bias=True)
          (key): Linear(in_features=512, out_features=512, bias=False)
          (value): Linear(in_features=512, out_features=512, bias=True)
          (out): Linear(in_features=512, out_features=512, bias=True)
        )
        (attn_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (mlp): Sequential(
          (0): Linear(in_features=512, out_features=2048, bias=True)
          (1): GELU(approximate='none')
          (2): Linear(in_features=2048, out_features=512, bias=True)
        )
        (mlp_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
      (4): ResidualAttentionBlock(
        (attn): MultiHeadAttention(
          (query): Linear(in_features=512, out_features=512, bias=True)
          (key): Linear(in_features=512, out_features=512, bias=False)
          (value): Linear(in_features=512, out_features=512, bias=True)
          (out): Linear(in_features=512, out_features=512, bias=True)
        )
        (attn_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (mlp): Sequential(
          (0): Linear(in_features=512, out_features=2048, bias=True)
          (1): GELU(approximate='none')
          (2): Linear(in_features=2048, out_features=512, bias=True)
        )
        (mlp_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
      (5): ResidualAttentionBlock(
        (attn): MultiHeadAttention(
          (query): Linear(in_features=512, out_features=512, bias=True)
          (key): Linear(in_features=512, out_features=512, bias=False)
          (value): Linear(in_features=512, out_features=512, bias=True)
          (out): Linear(in_features=512, out_features=512, bias=True)
        )
        (attn_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (mlp): Sequential(
          (0): Linear(in_features=512, out_features=2048, bias=True)
          (1): GELU(approximate='none')
          (2): Linear(in_features=2048, out_features=512, bias=True)
        )
        (mlp_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
    )
    (ln_post): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
  )
  (decoder): TextDecoder(
    (token_embedding): Embedding(51865, 512)
    (blocks): ModuleList(
      (0): ResidualAttentionBlock(
        (attn): MultiHeadAttention(
          (query): Linear(in_features=512, out_features=512, bias=True)
          (key): Linear(in_features=512, out_features=512, bias=False)
          (value): Linear(in_features=512, out_features=512, bias=True)
          (out): Linear(in_features=512, out_features=512, bias=True)
        )
        (attn_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (cross_attn): MultiHeadAttention(
          (query): Linear(in_features=512, out_features=512, bias=True)
          (key): Linear(in_features=512, out_features=512, bias=False)
          (value): Linear(in_features=512, out_features=512, bias=True)
          (out): Linear(in_features=512, out_features=512, bias=True)
        )
        (cross_attn_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (mlp): Sequential(
          (0): Linear(in_features=512, out_features=2048, bias=True)
          (1): GELU(approximate='none')
          (2): Linear(in_features=2048, out_features=512, bias=True)
        )
        (mlp_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
      (1): ResidualAttentionBlock(
        (attn): MultiHeadAttention(
          (query): Linear(in_features=512, out_features=512, bias=True)
          (key): Linear(in_features=512, out_features=512, bias=False)
          (value): Linear(in_features=512, out_features=512, bias=True)
          (out): Linear(in_features=512, out_features=512, bias=True)
        )
        (attn_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (cross_attn): MultiHeadAttention(
          (query): Linear(in_features=512, out_features=512, bias=True)
          (key): Linear(in_features=512, out_features=512, bias=False)
          (value): Linear(in_features=512, out_features=512, bias=True)
          (out): Linear(in_features=512, out_features=512, bias=True)
        )
        (cross_attn_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (mlp): Sequential(
          (0): Linear(in_features=512, out_features=2048, bias=True)
          (1): GELU(approximate='none')
          (2): Linear(in_features=2048, out_features=512, bias=True)
        )
        (mlp_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
      (2): ResidualAttentionBlock(
        (attn): MultiHeadAttention(
          (query): Linear(in_features=512, out_features=512, bias=True)
          (key): Linear(in_features=512, out_features=512, bias=False)
          (value): Linear(in_features=512, out_features=512, bias=True)
          (out): Linear(in_features=512, out_features=512, bias=True)
        )
        (attn_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (cross_attn): MultiHeadAttention(
          (query): Linear(in_features=512, out_features=512, bias=True)
          (key): Linear(in_features=512, out_features=512, bias=False)
          (value): Linear(in_features=512, out_features=512, bias=True)
          (out): Linear(in_features=512, out_features=512, bias=True)
        )
        (cross_attn_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (mlp): Sequential(
          (0): Linear(in_features=512, out_features=2048, bias=True)
          (1): GELU(approximate='none')
          (2): Linear(in_features=2048, out_features=512, bias=True)
        )
        (mlp_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
      (3): ResidualAttentionBlock(
        (attn): MultiHeadAttention(
          (query): Linear(in_features=512, out_features=512, bias=True)
          (key): Linear(in_features=512, out_features=512, bias=False)
          (value): Linear(in_features=512, out_features=512, bias=True)
          (out): Linear(in_features=512, out_features=512, bias=True)
        )
        (attn_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (cross_attn): MultiHeadAttention(
          (query): Linear(in_features=512, out_features=512, bias=True)
          (key): Linear(in_features=512, out_features=512, bias=False)
          (value): Linear(in_features=512, out_features=512, bias=True)
          (out): Linear(in_features=512, out_features=512, bias=True)
        )
        (cross_attn_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (mlp): Sequential(
          (0): Linear(in_features=512, out_features=2048, bias=True)
          (1): GELU(approximate='none')
          (2): Linear(in_features=2048, out_features=512, bias=True)
        )
        (mlp_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
      (4): ResidualAttentionBlock(
        (attn): MultiHeadAttention(
          (query): Linear(in_features=512, out_features=512, bias=True)
          (key): Linear(in_features=512, out_features=512, bias=False)
          (value): Linear(in_features=512, out_features=512, bias=True)
          (out): Linear(in_features=512, out_features=512, bias=True)
        )
        (attn_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (cross_attn): MultiHeadAttention(
          (query): Linear(in_features=512, out_features=512, bias=True)
          (key): Linear(in_features=512, out_features=512, bias=False)
          (value): Linear(in_features=512, out_features=512, bias=True)
          (out): Linear(in_features=512, out_features=512, bias=True)
        )
        (cross_attn_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (mlp): Sequential(
          (0): Linear(in_features=512, out_features=2048, bias=True)
          (1): GELU(approximate='none')
          (2): Linear(in_features=2048, out_features=512, bias=True)
        )
        (mlp_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
      (5): ResidualAttentionBlock(
        (attn): MultiHeadAttention(
          (query): Linear(in_features=512, out_features=512, bias=True)
          (key): Linear(in_features=512, out_features=512, bias=False)
          (value): Linear(in_features=512, out_features=512, bias=True)
          (out): Linear(in_features=512, out_features=512, bias=True)
        )
        (attn_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (cross_attn): MultiHeadAttention(
          (query): Linear(in_features=512, out_features=512, bias=True)
          (key): Linear(in_features=512, out_features=512, bias=False)
          (value): Linear(in_features=512, out_features=512, bias=True)
          (out): Linear(in_features=512, out_features=512, bias=True)
        )
        (cross_attn_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (mlp): Sequential(
          (0): Linear(in_features=512, out_features=2048, bias=True)
          (1): GELU(approximate='none')
          (2): Linear(in_features=2048, out_features=512, bias=True)
        )
        (mlp_ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
    )
    (ln): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
  )
)

spolisetty · January 6, 2023, 6:14pm

Hi,

I hope the following document is useful to you.

github.com

triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md

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