add model repo tlg

README.md

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+### usage
+
+```
+tritonserver --model-repository $model_repo_path \
+            --pinned-memory-pool-byte-size=512000000 \
+            --cuda-memory-pool-byte-size=0:1024000000 \
+            --http-port=18000 \
+            --metrics-port=18001 \
+            --grpc-port=18002 
+```
+

model_repo_cuda_decoder/attention_rescoring/config.pbtxt

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+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: "attention_rescoring"
+platform: "ensemble"
+max_batch_size: 16
+
+input [
+  {
+    name: "WAV"
+    data_type: TYPE_FP32
+    dims: [-1]
+  },
+  {
+    name: "WAV_LENS"
+    data_type: TYPE_INT32
+    dims: [1]
+  }
+]
+
+output [
+  {
+    name: "TRANSCRIPTS"
+    data_type: TYPE_STRING
+    dims: [1]
+  }
+]
+
+ensemble_scheduling {
+ step [
+   {
+    model_name: "feature_extractor"
+    model_version: -1
+    input_map {
+      key: "wav"
+      value: "WAV"
+    }
+    input_map {
+      key: "wav_lens"
+      value: "WAV_LENS"
+    }
+    output_map {
+      key: "speech"
+      value: "SPEECH"
+    }
+    output_map {
+      key: "speech_lengths"
+      value: "SPEECH_LENGTHS"
+    }
+   },
+   {
+    model_name: "encoder"
+    model_version: -1
+    input_map {
+      key: "speech"
+      value: "SPEECH"
+    }
+    input_map {
+      key: "speech_lengths"
+      value: "SPEECH_LENGTHS"
+    }
+    output_map {
+      key: "encoder_out"
+      value: "encoder_out"
+    }
+    output_map {
+      key: "encoder_out_lens"
+      value: "encoder_out_lens"
+    }
+    output_map {
+        key: "beam_log_probs"
+        value: "beam_log_probs"
+    }
+    output_map {
+        key: "beam_log_probs_idx"
+        value: "beam_log_probs_idx"
+    }
+    output_map {
+        key: "ctc_log_probs"
+        value: "ctc_log_probs"
+    }
+  },
+  {
+      model_name: "scoring"
+      model_version: -1
+      input_map {
+          key: "encoder_out"
+          value: "encoder_out"
+      }
+      input_map {
+          key: "encoder_out_lens"
+          value: "encoder_out_lens"
+      }
+      input_map {
+          key: "batch_log_probs"
+          value: "beam_log_probs"
+      }
+      input_map {
+          key: "batch_log_probs_idx"
+          value: "beam_log_probs_idx"
+      }
+      input_map {
+        key: "ctc_log_probs"
+        value: "ctc_log_probs"
+      }
+      output_map {
+          key: "OUTPUT0"
+          value: "TRANSCRIPTS"
+      }
+  }
+ ]
+}

model_repo_cuda_decoder/attention_rescoring/config.pbtxt.template

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+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: "attention_rescoring"
+platform: "ensemble"
+max_batch_size: MAX_BATCH
+
+input [
+  {
+    name: "WAV"
+    data_type: TYPE_FP32
+    dims: [-1]
+  },
+  {
+    name: "WAV_LENS"
+    data_type: TYPE_INT32
+    dims: [1]
+  }
+]
+
+output [
+  {
+    name: "TRANSCRIPTS"
+    data_type: TYPE_STRING
+    dims: [1]
+  }
+]
+
+ensemble_scheduling {
+ step [
+   {
+    model_name: "feature_extractor"
+    model_version: -1
+    input_map {
+      key: "wav"
+      value: "WAV"
+    }
+    input_map {
+      key: "wav_lens"
+      value: "WAV_LENS"
+    }
+    output_map {
+      key: "speech"
+      value: "SPEECH"
+    }
+    output_map {
+      key: "speech_lengths"
+      value: "SPEECH_LENGTHS"
+    }
+   },
+   {
+    model_name: "encoder"
+    model_version: -1
+    input_map {
+      key: "speech"
+      value: "SPEECH"
+    }
+    input_map {
+      key: "speech_lengths"
+      value: "SPEECH_LENGTHS"
+    }
+    output_map {
+      key: "encoder_out"
+      value: "encoder_out"
+    }
+    output_map {
+      key: "encoder_out_lens"
+      value: "encoder_out_lens"
+    }
+    output_map {
+        key: "beam_log_probs"
+        value: "beam_log_probs"
+    }
+    output_map {
+        key: "beam_log_probs_idx"
+        value: "beam_log_probs_idx"
+    }
+    output_map {
+        key: "ctc_log_probs"
+        value: "ctc_log_probs"
+    }
+  },
+  {
+      model_name: "scoring"
+      model_version: -1
+      input_map {
+          key: "encoder_out"
+          value: "encoder_out"
+      }
+      input_map {
+          key: "encoder_out_lens"
+          value: "encoder_out_lens"
+      }
+      input_map {
+          key: "batch_log_probs"
+          value: "beam_log_probs"
+      }
+      input_map {
+          key: "batch_log_probs_idx"
+          value: "beam_log_probs_idx"
+      }
+      input_map {
+        key: "ctc_log_probs"
+        value: "ctc_log_probs"
+      }
+      output_map {
+          key: "OUTPUT0"
+          value: "TRANSCRIPTS"
+      }
+  }
+ ]
+}

model_repo_cuda_decoder/decoder/1/decoder_fp16.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ff5783dac46388e38ed0c4d81a1750a4be966d3143394747f4c2e5143cd682f9
+size 26132742

model_repo_cuda_decoder/decoder/config.pbtxt

@@ -0,0 +1,68 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: "decoder"
+backend: "onnxruntime"
+default_model_filename: "decoder_fp16.onnx"
+
+max_batch_size: 16
+input [
+  {
+    name: "encoder_out"
+    data_type: TYPE_FP16
+    dims: [-1, 256]
+  },
+  {
+    name: "encoder_out_lens"
+    data_type: TYPE_INT32
+    dims: [1]
+    reshape: { shape: [ ] }
+  },
+  {
+    name: "hyps_pad_sos_eos"
+    data_type: TYPE_INT64
+    dims: [10, -1]
+  },
+ {
+    name: "hyps_lens_sos"
+    data_type: TYPE_INT32
+    dims: [10]
+  },
+  {
+    name: "ctc_score"
+    data_type: TYPE_FP16
+    dims: [10]
+  }
+]
+
+output [
+   {
+    name: "best_index"
+    data_type: TYPE_INT64
+    dims: [1]
+    reshape: { shape: [ ] }
+  }
+]
+
+dynamic_batching {
+  max_queue_delay_microseconds: 0
+  }
+
+instance_group [
+    {
+      count: 2
+      kind: KIND_GPU
+    }
+]
+

model_repo_cuda_decoder/decoder/config.pbtxt.template

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+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: "decoder"
+backend: "onnxruntime"
+default_model_filename: "decoder_fp16.onnx"
+
+max_batch_size: MAX_BATCH
+input [
+  {
+    name: "encoder_out"
+    data_type: TYPE_FP16
+    dims: [-1, D_MODEL]
+  },
+  {
+    name: "encoder_out_lens"
+    data_type: TYPE_INT32
+    dims: [1]
+    reshape: { shape: [ ] }
+  },
+  {
+    name: "hyps_pad_sos_eos"
+    data_type: TYPE_INT64
+    dims: [BEAM_SIZE, -1]
+  },
+ {
+    name: "hyps_lens_sos"
+    data_type: TYPE_INT32
+    dims: [BEAM_SIZE]
+  },
+  {
+    name: "ctc_score"
+    data_type: TYPE_FP16
+    dims: [BEAM_SIZE]
+  }
+]
+
+output [
+   {
+    name: "best_index"
+    data_type: TYPE_INT64
+    dims: [1]
+    reshape: { shape: [ ] }
+  }
+]
+
+dynamic_batching {
+  max_queue_delay_microseconds: MAX_DELAY
+  }
+
+instance_group [
+    {
+      count: INSTANCE_NUM
+      kind: KIND_GPU
+    }
+]
+

model_repo_cuda_decoder/encoder/1/encoder_fp16.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:12eca8d0bd041a42405fb65037717b823ae2769157a1fcb78c529f18e1c10d76
+size 72186937

model_repo_cuda_decoder/encoder/config.pbtxt

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+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: "encoder"
+backend: "onnxruntime"
+default_model_filename: "encoder_fp16.onnx"
+
+max_batch_size: 16
+input [
+  {
+    name: "speech"
+    data_type: TYPE_FP16
+    dims: [-1, 80]
+  },
+  {
+    name: "speech_lengths"
+    data_type: TYPE_INT32
+    dims: [1]
+    reshape: { shape: [ ] }
+  }
+]
+
+output [
+  {
+    name: "encoder_out"
+    data_type: TYPE_FP16
+    dims: [-1, 256]
+  },
+  {
+    name: "encoder_out_lens"
+    data_type: TYPE_INT32
+    dims: [1]
+    reshape: { shape: [ ] }
+  },
+  {
+    name: "ctc_log_probs"
+    data_type: TYPE_FP16
+    dims: [-1, 4233]
+  },
+  {
+    name: "beam_log_probs"
+    data_type: TYPE_FP16
+    dims: [-1, 10]
+  },
+  {
+    name: "beam_log_probs_idx"
+    data_type: TYPE_INT64
+    dims: [-1, 10]
+  }
+]
+
+dynamic_batching {
+  max_queue_delay_microseconds: 0
+  }
+
+
+instance_group [
+    {
+      count: 2
+      kind: KIND_GPU
+    }
+]

model_repo_cuda_decoder/encoder/config.pbtxt.template

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+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: "encoder"
+backend: "onnxruntime"
+default_model_filename: "encoder_fp16.onnx"
+
+max_batch_size: MAX_BATCH
+input [
+  {
+    name: "speech"
+    data_type: TYPE_FP16
+    dims: [-1, 80]
+  },
+  {
+    name: "speech_lengths"
+    data_type: TYPE_INT32
+    dims: [1]
+    reshape: { shape: [ ] }
+  }
+]
+
+output [
+  {
+    name: "encoder_out"
+    data_type: TYPE_FP16
+    dims: [-1, D_MODEL]
+  },
+  {
+    name: "encoder_out_lens"
+    data_type: TYPE_INT32
+    dims: [1]
+    reshape: { shape: [ ] }
+  },
+  {
+    name: "ctc_log_probs"
+    data_type: TYPE_FP16
+    dims: [-1, VOCAB_SIZE]
+  },
+  {
+    name: "beam_log_probs"
+    data_type: TYPE_FP16
+    dims: [-1, BEAM_SIZE]
+  },
+  {
+    name: "beam_log_probs_idx"
+    data_type: TYPE_INT64
+    dims: [-1, BEAM_SIZE]
+  }
+]
+
+dynamic_batching {
+  max_queue_delay_microseconds: MAX_DELAY
+  }
+
+
+instance_group [
+    {
+      count: INSTANCE_NUM
+      kind: KIND_GPU
+    }
+]

model_repo_cuda_decoder/feature_extractor/1/model.py

@@ -0,0 +1,158 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import triton_python_backend_utils as pb_utils
+from torch.utils.dlpack import to_dlpack
+import torch
+import numpy as np
+import kaldifeat
+import _kaldifeat
+from typing import List
+import json
+
+class Fbank(torch.nn.Module):
+    def __init__(self, opts):
+        super(Fbank, self).__init__()
+        self.fbank = kaldifeat.Fbank(opts)
+
+    def forward(self, waves: List[torch.Tensor]):
+        return self.fbank(waves)
+
+
+class TritonPythonModel:
+    """Your Python model must use the same class name. Every Python model
+    that is created must have "TritonPythonModel" as the class name.
+    """
+
+    def initialize(self, args):
+        """`initialize` is called only once when the model is being loaded.
+        Implementing `initialize` function is optional. This function allows
+        the model to initialize any state associated with this model.
+
+        Parameters
+        ----------
+        args : dict
+          Both keys and values are strings. The dictionary keys and values are:
+          * model_config: A JSON string containing the model configuration
+          * model_instance_kind: A string containing model instance kind
+          * model_instance_device_id: A string containing model instance device ID
+          * model_repository: Model repository path
+          * model_version: Model version
+          * model_name: Model name
+        """
+        self.model_config = model_config = json.loads(args['model_config'])
+        self.max_batch_size = max(model_config["max_batch_size"], 1)
+
+        if "GPU" in model_config["instance_group"][0]["kind"]:
+            self.device = "cuda"
+        else:
+            self.device = "cpu"
+
+        # Get OUTPUT0 configuration
+        output0_config = pb_utils.get_output_config_by_name(
+            model_config, "speech")
+        # Convert Triton types to numpy types
+        output0_dtype = pb_utils.triton_string_to_numpy(
+            output0_config['data_type'])
+        if output0_dtype == np.float32:
+            self.output0_dtype = torch.float32
+        else:
+            self.output0_dtype = torch.float16
+
+        # Get OUTPUT1 configuration
+        output1_config = pb_utils.get_output_config_by_name(
+            model_config, "speech_lengths")
+        # Convert Triton types to numpy types
+        self.output1_dtype = pb_utils.triton_string_to_numpy(
+            output1_config['data_type'])
+
+        params = self.model_config['parameters']
+        opts = kaldifeat.FbankOptions()
+        opts.frame_opts.dither = 0
+
+        for li in params.items():
+            key, value = li
+            value = value["string_value"]
+            if key == "num_mel_bins":
+                opts.mel_opts.num_bins = int(value)
+            elif key == "frame_shift_in_ms":
+                opts.frame_opts.frame_shift_ms = float(value)
+            elif key == "frame_length_in_ms":
+                opts.frame_opts.frame_length_ms = float(value)
+            elif key == "sample_rate":
+                opts.frame_opts.samp_freq = int(value)
+        opts.device = torch.device(self.device)
+        self.opts = opts
+        self.feature_extractor = Fbank(self.opts)
+        self.feature_size = opts.mel_opts.num_bins
+
+    def execute(self, requests):
+        """`execute` must be implemented in every Python model. `execute`
+        function receives a list of pb_utils.InferenceRequest as the only
+        argument. This function is called when an inference is requested
+        for this model.
+
+        Parameters
+        ----------
+        requests : list
+          A list of pb_utils.InferenceRequest
+
+        Returns
+        -------
+        list
+          A list of pb_utils.InferenceResponse. The length of this list must
+          be the same as `requests`
+        """
+        batch_count = []
+        total_waves = []
+        batch_len = []
+        responses = []
+        for request in requests:
+            input0 = pb_utils.get_input_tensor_by_name(request, "wav")
+            input1 = pb_utils.get_input_tensor_by_name(request, "wav_lens")
+
+            cur_b_wav = input0.as_numpy()
+            cur_b_wav = cur_b_wav * (1 << 15)  # b x -1
+            cur_b_wav_lens = input1.as_numpy()  # b x 1
+            cur_batch = cur_b_wav.shape[0]
+            cur_len = cur_b_wav.shape[1]
+            batch_count.append(cur_batch)
+            batch_len.append(cur_len)
+            for wav, wav_len in zip(cur_b_wav, cur_b_wav_lens):
+                wav_len = wav_len[0]
+                wav = torch.tensor(wav[0:wav_len], dtype=torch.float32,
+                                   device=self.device)
+                total_waves.append(wav)
+
+        features = self.feature_extractor(total_waves)
+        for b, l in zip(batch_count, batch_len):
+            expect_feat_len = _kaldifeat.num_frames(l, self.opts.frame_opts)
+            speech = torch.zeros((b, expect_feat_len, self.feature_size),
+                                 dtype=self.output0_dtype, device=self.device)
+            speech_lengths = torch.zeros((b, 1), dtype=torch.int32, device=self.device)
+            for i in range(b):
+                f = features.pop(0)
+                f_l = f.shape[0]
+                speech[i, 0: f_l, :] = f.to(self.output0_dtype)
+                speech_lengths[i][0] = f_l
+            # put speech feature on device will cause empty output
+            # we will follow this issue and now temporarily put it on cpu
+            speech = speech.cpu()
+            speech_lengths = speech_lengths.cpu()
+            out0 = pb_utils.Tensor.from_dlpack("speech", to_dlpack(speech))
+            out1 = pb_utils.Tensor.from_dlpack("speech_lengths",
+                                               to_dlpack(speech_lengths))
+            inference_response = pb_utils.InferenceResponse(output_tensors=[out0, out1])
+            responses.append(inference_response)
+        return responses

model_repo_cuda_decoder/feature_extractor/config.pbtxt

@@ -0,0 +1,72 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: "feature_extractor"
+backend: "python"
+max_batch_size: 16
+
+parameters [
+  {
+    key: "num_mel_bins",
+    value: { string_value: "80"}
+  },
+  {
+    key: "frame_shift_in_ms"
+    value: { string_value: "10"}
+  },
+  {
+    key: "frame_length_in_ms"
+    value: { string_value: "25"}
+  },
+  {
+    key: "sample_rate"
+    value: { string_value: "16000"}
+  }
+
+]
+
+input [
+  {
+    name: "wav"
+    data_type: TYPE_FP32
+    dims: [-1]
+  },
+  {
+    name: "wav_lens"
+    data_type: TYPE_INT32
+    dims: [1]
+  }
+]
+
+output [
+  {
+    name: "speech"
+    data_type: TYPE_FP16
+    dims: [-1, 80]
+  },
+  {
+    name: "speech_lengths"
+    data_type: TYPE_INT32
+    dims: [1]
+  }
+]
+
+dynamic_batching {
+  }
+instance_group [
+    {
+      count: 2
+      kind: KIND_GPU
+    }
+]

model_repo_cuda_decoder/feature_extractor/config.pbtxt.template

@@ -0,0 +1,72 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: "feature_extractor"
+backend: "python"
+max_batch_size: MAX_BATCH
+
+parameters [
+  {
+    key: "num_mel_bins",
+    value: { string_value: "80"}
+  },
+  {
+    key: "frame_shift_in_ms"
+    value: { string_value: "10"}
+  },
+  {
+    key: "frame_length_in_ms"
+    value: { string_value: "25"}
+  },
+  {
+    key: "sample_rate"
+    value: { string_value: "16000"}
+  }
+
+]
+
+input [
+  {
+    name: "wav"
+    data_type: TYPE_FP32
+    dims: [-1]
+  },
+  {
+    name: "wav_lens"
+    data_type: TYPE_INT32
+    dims: [1]
+  }
+]
+
+output [
+  {
+    name: "speech"
+    data_type: TYPE_FP16
+    dims: [-1, 80]
+  },
+  {
+    name: "speech_lengths"
+    data_type: TYPE_INT32
+    dims: [1]
+  }
+]
+
+dynamic_batching {
+  }
+instance_group [
+    {
+      count: INSTANCE_NUM
+      kind: KIND_GPU
+    }
+]

model_repo_cuda_decoder/scoring/1/decoder.py

@@ -0,0 +1,132 @@
+import os
+import torch
+from typing import List
+from riva.asrlib.decoder.python_decoder import (BatchedMappedDecoderCuda,
+                                                BatchedMappedDecoderCudaConfig)
+
+def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
+    """Make mask tensor containing indices of padded part.
+    See description of make_non_pad_mask.
+    Args:
+        lengths (torch.Tensor): Batch of lengths (B,).
+    Returns:
+        torch.Tensor: Mask tensor containing indices of padded part.
+    Examples:
+        >>> lengths = [5, 3, 2]
+        >>> make_pad_mask(lengths)
+        masks = [[0, 0, 0, 0 ,0],
+                 [0, 0, 0, 1, 1],
+                 [0, 0, 1, 1, 1]]
+    """
+    batch_size = lengths.size(0)
+    max_len = max_len if max_len > 0 else lengths.max().item()
+    seq_range = torch.arange(0,
+                             max_len,
+                             dtype=torch.int64,
+                             device=lengths.device)
+    seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
+    seq_length_expand = lengths.unsqueeze(-1)
+    mask = seq_range_expand >= seq_length_expand
+    return mask
+
+def remove_duplicates_and_blank(hyp: List[int],
+                                eos: int,
+                                blank_id: int = 0) -> List[int]:
+    new_hyp: List[int] = []
+    cur = 0
+    while cur < len(hyp):
+        if hyp[cur] != blank_id and hyp[cur] != eos:
+            new_hyp.append(hyp[cur])
+        prev = cur
+        while cur < len(hyp) and hyp[cur] == hyp[prev]:
+            cur += 1
+    return new_hyp
+
+def ctc_greedy_search(ctc_probs, encoder_out_lens, vocabulary, blank_id, eos):
+    batch_size, maxlen = ctc_probs.size()[:2]
+    topk_prob, topk_index = ctc_probs.topk(1, dim=2)  # (B, maxlen, 1)
+    topk_index = topk_index.view(batch_size, maxlen)  # (B, maxlen)
+    mask = make_pad_mask(encoder_out_lens, maxlen)  # (B, maxlen)
+    topk_index = topk_index.cpu().masked_fill_(mask, eos)  # (B, maxlen)
+    hyps = [hyp.tolist() for hyp in topk_index]
+    hyps = [remove_duplicates_and_blank(hyp, eos, blank_id) for hyp in hyps]
+    total_hyps = []
+    for hyp in hyps:
+        total_hyps.append("".join([vocabulary[i] for i in hyp]))
+    return total_hyps
+
+def load_word_symbols(path):
+    word_id_to_word_str = {}
+    with open(path, "rt", encoding="utf-8") as fh:
+        for line in fh:
+            word_str, word_id = line.rstrip().split()
+            word_id_to_word_str[int(word_id)] = word_str
+    return word_id_to_word_str
+
+class RivaWFSTDecoder:
+    def __init__(self, vocab_size, tlg_dir, config_dict, beam_size=8.0):
+        config = BatchedMappedDecoderCudaConfig()
+        config.online_opts.decoder_opts.lattice_beam = beam_size
+
+        config.online_opts.lattice_postprocessor_opts.acoustic_scale = config_dict['acoustic_scale'] # noqa
+        config.n_input_per_chunk = config_dict['n_input_per_chunk']
+        config.online_opts.decoder_opts.default_beam = config_dict['default_beam']
+        config.online_opts.decoder_opts.max_active = config_dict['max_active']
+        config.online_opts.determinize_lattice = config_dict['determinize_lattice']
+        config.online_opts.max_batch_size = config_dict['max_batch_size']
+        config.online_opts.num_channels = config_dict['num_channels']
+        config.online_opts.frame_shift_seconds = config_dict['frame_shift_seconds']
+        config.online_opts.lattice_postprocessor_opts.lm_scale = config_dict['lm_scale']
+        config.online_opts.lattice_postprocessor_opts.word_ins_penalty = config_dict['word_ins_penalty'] # noqa
+
+        config.online_opts.lattice_postprocessor_opts.nbest = beam_size
+
+        self.decoder = BatchedMappedDecoderCuda(
+            config, os.path.join(tlg_dir, "TLG.fst"),
+            os.path.join(tlg_dir, "words.txt"), vocab_size
+        )
+        self.word_id_to_word_str = load_word_symbols(os.path.join(tlg_dir, "words.txt"))
+        self.nbest = beam_size
+
+    def decode_nbest(self, logits, length):
+        logits = logits.to(torch.float32).contiguous()
+        sequence_lengths_tensor = length.to(torch.long).to('cpu').contiguous()
+        before = logits.shape
+        if logits.shape[0] == 1:
+            logits = logits.repeat(2,1,1)
+            sequence_lengths_tensor = sequence_lengths_tensor.repeat(2)
+        print(before, logits.shape)
+        results = self.decoder.decode_nbest(logits, sequence_lengths_tensor)
+        if logits.shape[0] == 1:
+            results = results[0:1]        
+        total_hyps, total_hyps_id = [], []
+        for nbest_sentences in results:
+            nbest_list, nbest_id_list = []
+            for sent in nbest_sentences:
+                # subtract 1 to get the label id, since fst decoder adds 1 to the label id
+                hyp_ids = [label - 1 for label in sent.ilabels]
+                new_hyp = remove_duplicates_and_blank(hyp_ids, eos=self.vocab_size-1, blank_id=0)
+                nbest_id_list.append(new_hyp)
+
+                hyp = "".join(self.word_id_to_word_str[word] for word in sent.words if word != 0)
+                nbest_list.append(hyp)
+
+            total_hyps.append(nbest_list)
+            total_hyps_id.append(nbest_id_list)
+        return total_hyps, total_hyps_id
+
+    def decode_mbr(self, logits, length):
+        logits = logits.to(torch.float32).contiguous()
+        sequence_lengths_tensor = length.to(torch.long).to('cpu').contiguous()
+        if logits.shape[0] == 1:
+            logits = logits.repeat(2,1,1)
+            sequence_lengths_tensor = sequence_lengths_tensor.repeat(2)
+        results = self.decoder.decode_mbr(logits, sequence_lengths_tensor)
+        if logits.shape[0] == 1:
+            results = results[0:1]
+        total_hyps = []
+        for sent in results:
+            hyp = [word[0] for word in sent]
+            hyp_zh = "".join(hyp)
+            total_hyps.append(hyp_zh)
+        return total_hyps

model_repo_cuda_decoder/scoring/1/model.py

@@ -0,0 +1,249 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import triton_python_backend_utils as pb_utils
+import numpy as np
+
+import torch
+from torch.utils.dlpack import from_dlpack
+import json
+import os
+import yaml
+from decoder import RivaWFSTDecoder, ctc_greedy_search
+
+class TritonPythonModel:
+    """Your Python model must use the same class name. Every Python model
+    that is created must have "TritonPythonModel" as the class name.
+    """
+
+    def initialize(self, args):
+        """`initialize` is called only once when the model is being loaded.
+        Implementing `initialize` function is optional. This function allows
+        the model to initialize any state associated with this model.
+
+        Parameters
+        ----------
+        args : dict
+          Both keys and values are strings. The dictionary keys and values are:
+          * model_config: A JSON string containing the model configuration
+          * model_instance_kind: A string containing model instance kind
+          * model_instance_device_id: A string containing model instance device ID
+          * model_repository: Model repository path
+          * model_version: Model version
+          * model_name: Model name
+        """
+        self.model_config = model_config = json.loads(args['model_config'])
+        self.max_batch_size = max(model_config["max_batch_size"], 1)
+
+        # Get OUTPUT0 configuration
+        output0_config = pb_utils.get_output_config_by_name(
+            model_config, "OUTPUT0")
+        # Convert Triton types to numpy types
+        self.out0_dtype = pb_utils.triton_string_to_numpy(
+            output0_config['data_type'])
+
+        # Get INPUT configuration
+
+        encoder_config = pb_utils.get_input_config_by_name(
+            model_config, "encoder_out")
+        self.data_type = pb_utils.triton_string_to_numpy(
+            encoder_config['data_type'])
+
+        self.feature_size = encoder_config['dims'][-1]
+
+        self.lm = None
+        self.init_decoder(self.model_config['parameters'])
+        print('Initialized Scoring Module!')
+
+    def init_decoder(self, parameters):
+        import multiprocessing
+        num_processes = multiprocessing.cpu_count()
+        cutoff_prob = 0.9999
+        blank_id = 0
+        alpha = 2.0
+        beta = 1.0
+        ignore_id = -1
+        bidecoder = 0
+        lm_path, vocab_path = None, None
+        for li in parameters.items():
+            key, value = li
+            value = value["string_value"]
+            if key == "num_processes":
+                num_processes = int(value)
+            elif key == "blank_id":
+                blank_id = int(value)
+            elif key == "cutoff_prob":
+                cutoff_prob = float(value)
+            elif key == "lm_path":
+                lm_path = value
+            elif key == "alpha":
+                alpha = float(value)
+            elif key == "beta":
+                beta = float(value)
+            elif key == "vocabulary":
+                vocab_path = value
+            elif key == 'ignore_id':
+                ignore_id = int(value)
+            elif key == "bidecoder":
+                bidecoder = int(value)
+            elif key == "tlg_decoding_config":
+                with open(str(value), 'rb') as f:
+                    self.tlg_decoding_config = yaml.load(f, Loader=yaml.Loader)
+            elif key == "tlg_dir":
+                self.tlg_dir = str(value)
+            elif key == "decoding_method":
+                self.decoding_method = str(value)
+            elif key == "attention_rescoring":
+                self.rescore = int(value)
+            elif key == "beam_size":
+                self.beam_size = int(value)
+
+        self.num_processes = num_processes
+        self.cutoff_prob = cutoff_prob
+        self.blank_id = blank_id
+        _, vocab = self.load_vocab(vocab_path)
+        if lm_path and os.path.exists(lm_path):
+            self.lm = Scorer(alpha, beta, lm_path, vocab)
+            print("Successfully load language model!")
+        self.vocabulary = vocab
+        self.bidecoder = bidecoder
+        sos = eos = len(vocab) - 1
+        self.sos = sos
+        self.eos = eos
+        self.ignore_id = ignore_id
+
+        if self.decoding_method == "tlg":
+            self.decoder = RivaWFSTDecoder(len(self.vocabulary),
+                                           self.tlg_dir,
+                                           self.tlg_decoding_config,
+                                           self.beam_size)
+
+    def load_vocab(self, vocab_file):
+        """
+        load lang_char.txt
+        """
+        id2vocab = {}
+        with open(vocab_file, "r", encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                char, id = line.split()
+                id2vocab[int(id)] = char
+        vocab = [0] * len(id2vocab)
+        for id, char in id2vocab.items():
+            vocab[id] = char
+        return id2vocab, vocab
+
+    def collect_inputs(self, requests):
+        encoder_out_list, encoder_out_lens_list, ctc_log_probs_list, batch_count_list = [], [], [], [] # noqa
+        for request in requests:
+            # Perform inference on the request and append it to responses list...
+            in_0 = pb_utils.get_input_tensor_by_name(request, "encoder_out")
+            in_1 = pb_utils.get_input_tensor_by_name(request, "encoder_out_lens")
+            in_2 = pb_utils.get_input_tensor_by_name(request, "ctc_log_probs")
+
+            in_0_tensor = from_dlpack(in_0.to_dlpack())
+            in_1_tensor = from_dlpack(in_1.to_dlpack())
+            in_2_tensor = from_dlpack(in_2.to_dlpack())
+
+            encoder_out_list.append(in_0_tensor)
+            encoder_out_lens_list.append(in_1_tensor)
+            ctc_log_probs_list.append(in_2_tensor)
+
+            batch_count_list.append(in_0_tensor.shape[0])
+
+        encoder_tensors, logits_tensors = [], []
+        for encoder_tensor, logits_tensor in zip(encoder_out_list, ctc_log_probs_list):
+            encoder_tensors += [item.squeeze(0) for item in encoder_tensor.split(1)]
+            logits_tensors += [item.squeeze(0) for item in logits_tensor.split(1)]
+        encoder_out = torch.nn.utils.rnn.pad_sequence(encoder_tensors,
+                                                      batch_first=True,
+                                                      padding_value=0.0)
+        logits = torch.nn.utils.rnn.pad_sequence(logits_tensors,
+                                                 batch_first=True,
+                                                 padding_value=0.0)
+        encoder_out_len = torch.cat(encoder_out_lens_list, dim=0)
+        return encoder_out, encoder_out_len, logits, batch_count_list
+
+    def rescore_hyps(self, total_hyps, total_tokens, encoder_out, encoder_out_len):
+        """
+        Rescore the hypotheses with attention rescoring
+        """
+        # TODO: add attention rescoring
+        return total_hyps
+
+    def prepare_response(self, hyps, batch_count_list):
+        """
+        Prepare the response
+        """
+        responses = []
+        st = 0
+        for b in batch_count_list:
+            sents = np.array(hyps[st:st + b])
+            out0 = pb_utils.Tensor("OUTPUT0", sents.astype(self.out0_dtype))
+            inference_response = pb_utils.InferenceResponse(output_tensors=[out0])
+            responses.append(inference_response)
+            st += b
+        return responses
+
+    def execute(self, requests):
+        """`execute` must be implemented in every Python model. `execute`
+        function receives a list of pb_utils.InferenceRequest as the only
+        argument. This function is called when an inference is requested
+        for this model.
+
+        Parameters
+        ----------
+        requests : list
+          A list of pb_utils.InferenceRequest
+
+        Returns
+        -------
+        list
+          A list of pb_utils.InferenceResponse. The length of this list must
+          be the same as `requests`
+        """
+        # Every Python backend must iterate through list of requests and create
+        # an instance of pb_utils.InferenceResponse class for each of them. You
+        # should avoid storing any of the input Tensors in the class attributes
+        # as they will be overridden in subsequent inference requests. You can
+        # make a copy of the underlying NumPy array and store it if it is
+        # required.
+        encoder_out, encoder_out_len, ctc_log_probs, batch_count = self.collect_inputs(requests) # noqa
+        ctc_log_probs = ctc_log_probs.cuda()
+        if self.decoding_method == "tlg_mbr":
+            total_hyps = self.decoder.decode_mbr(ctc_log_probs, encoder_out_len)
+            # list(str), list((float), list(int)) # TODO: add token_ids, time stamps
+        elif self.decoding_method == "ctc_greedy_search":
+            total_hyps = ctc_greedy_search(ctc_log_probs, encoder_out_len,
+                                           self.vocabulary, self.blank_id, self.eos)
+        elif self.decoding_method == "tlg":
+            nbest_hyps, nbest_ids = self.decoder.decode_nbest(encoder_out, encoder_out_len)
+            total_hyps = [nbest[0] for nbest in nbest_hyps]
+
+        if self.decoding_method == "tlg" and self.rescore:
+            assert self.beam_size > 1, "Beam size must be greater than 1 for rescoring"
+            selected_ids = self.rescore_hyps(nbest_ids,
+                                             encoder_out,
+                                             encoder_out_len)
+            total_hyps = [nbest[i] for nbest, i in zip(nbest_hyps, selected_ids)]
+
+        responses = self.prepare_response(total_hyps, batch_count)
+        return responses
+
+    def finalize(self):
+        """`finalize` is called only once when the model is being unloaded.
+        Implementing `finalize` function is optional. This function allows
+        the model to perform any necessary clean ups before exit.
+        """
+        print('Cleaning up...')

model_repo_cuda_decoder/scoring/1/wfst_decoding_config.yaml

@@ -0,0 +1,10 @@
+acoustic_scale: 10.0
+n_input_per_chunk: 50
+default_beam: 17.0
+max_active: 7000
+determinize_lattice: True
+max_batch_size: 800
+num_channels: 800
+frame_shift_seconds: 0.04
+lm_scale: 5.0
+word_ins_penalty: 0.0

model_repo_cuda_decoder/scoring/config.pbtxt

@@ -0,0 +1,100 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: "scoring"
+backend: "python"
+max_batch_size: 16
+
+parameters [
+  {
+    key: "vocabulary",
+    value: { string_value: "./model_repo_cuda_decoder/scoring/units.txt"}
+  },
+  {
+    key: "tlg_dir",
+    value: { string_value: "./model_repo_cuda_decoder/scoring/1/lang"}
+  },
+  {
+    key: "tlg_decoding_config",
+    value: { string_value: "./model_repo_cuda_decoder/scoring/1/wfst_decoding_config.yaml"}
+  },
+  {
+    key: "beam_size",
+    value: { string_value: "10"}
+  },
+  {
+    key: "decoding_method",
+    value: { string_value: "tlg"} # tlg, ctc_greedy_search, cpu_ctc_beam_search, cuda_ctc_beam_search
+  },
+  {
+    key: "attention_rescoring",
+    value: { string_value: "0"}
+  },
+  {
+    key: "bidecoder",
+    value: { string_value: "0"} # for attention rescoring decoder
+  },
+  {
+    key: "lm_path"
+    value: { string_value: "None"} # for ctc beam search methods
+  }
+]
+
+
+input [
+  {
+    name: "encoder_out"
+    data_type: TYPE_FP16
+    dims: [-1, 256]
+  },
+  {
+    name: "encoder_out_lens"
+    data_type: TYPE_INT32
+    dims: [1]
+    reshape: { shape: [ ] }
+  },
+  {
+    name: "batch_log_probs"
+    data_type: TYPE_FP16
+    dims: [-1, 10]
+  },
+  {
+    name: "batch_log_probs_idx"
+    data_type: TYPE_INT64
+    dims: [-1, 10]
+  },
+  {
+    name: "ctc_log_probs"
+    data_type: TYPE_FP16
+    dims: [-1, 4233]
+  }
+]
+
+output [
+  {
+    name: "OUTPUT0"
+    data_type: TYPE_STRING
+    dims: [1]
+  }
+]
+
+dynamic_batching {
+  max_queue_delay_microseconds: 0
+  }
+instance_group [
+    {
+      count: 2
+      kind: KIND_CPU
+    }
+  ]

model_repo_cuda_decoder/scoring/config.pbtxt.template

@@ -0,0 +1,100 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: "scoring"
+backend: "python"
+max_batch_size: MAX_BATCH
+
+parameters [
+  {
+    key: "vocabulary",
+    value: { string_value: "./model_repo_cuda_decoder/scoring/units.txt"}
+  },
+  {
+    key: "tlg_dir",
+    value: { string_value: "./model_repo_cuda_decoder/scoring/1/lang"}
+  },
+  {
+    key: "tlg_decoding_config",
+    value: { string_value: "./model_repo_cuda_decoder/scoring/1/wfst_decoding_config.yaml"}
+  },
+  {
+    key: "beam_size",
+    value: { string_value: "BEAM_SIZE"}
+  },
+  {
+    key: "decoding_method",
+    value: { string_value: "DECODING_METHOD"} # tlg, ctc_greedy_search, cpu_ctc_beam_search, cuda_ctc_beam_search
+  },
+  {
+    key: "attention_rescoring",
+    value: { string_value: "0"}
+  },
+  {
+    key: "bidecoder",
+    value: { string_value: "0"} # for attention rescoring decoder
+  },
+  {
+    key: "lm_path"
+    value: { string_value: "None"} # for ctc beam search methods
+  }
+]
+
+
+input [
+  {
+    name: "encoder_out"
+    data_type: TYPE_FP16
+    dims: [-1, D_MODEL]
+  },
+  {
+    name: "encoder_out_lens"
+    data_type: TYPE_INT32
+    dims: [1]
+    reshape: { shape: [ ] }
+  },
+  {
+    name: "batch_log_probs"
+    data_type: TYPE_FP16
+    dims: [-1, BEAM_SIZE]
+  },
+  {
+    name: "batch_log_probs_idx"
+    data_type: TYPE_INT64
+    dims: [-1, BEAM_SIZE]
+  },
+  {
+    name: "ctc_log_probs"
+    data_type: TYPE_FP16
+    dims: [-1, VOCAB_SIZE]
+  }
+]
+
+output [
+  {
+    name: "OUTPUT0"
+    data_type: TYPE_STRING
+    dims: [1]
+  }
+]
+
+dynamic_batching {
+  max_queue_delay_microseconds: MAX_DELAY
+  }
+instance_group [
+    {
+      count: INSTANCE_NUM
+      kind: KIND_CPU
+    }
+  ]

model_repo_cuda_decoder/scoring/units.txt

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requirements.txt

@@ -0,0 +1,2 @@
+riva-asrlib-decoder
+onnxmltools