.. _tp_inference_tutorial:

Inference with Tensor Parallelism [Beta]
===========================================================================

Before we start, let's install transformers.

.. code:: ipython3

    pip install transformers==4.26.0

For running model inference, we would need to trace the distributed
model. Before we run the inference, let’s get a checkpoint that we can
use. Let’s run the below block of code:

.. code:: ipython3

    import torch
    import torch_neuronx
    import transformers
    from transformers import AutoTokenizer, AutoModelForSequenceClassification

    name = "bert-base-cased-finetuned-mrpc"

    model = AutoModelForSequenceClassification.from_pretrained(name, torchscript=True)
    torch.save({"model":model.state_dict()}, "bert.pt")

If you already have a checkpoint from the tensor parallel training tutorial or by running
training from another source, feel free to skip the above step.

Once we have the checkpoint we are ready to trace the model and run
inference against it. Let’s look at the example below:

.. code:: ipython3

    import os
    import torch
    import torch_neuronx
    import transformers
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    from transformers.models.bert.modeling_bert import BertSelfAttention, BertSelfOutput

    import neuronx_distributed
    from neuronx_distributed.parallel_layers import layers, parallel_state


    def encode(tokenizer, *inputs, max_length=128, batch_size=1):
        tokens = tokenizer.encode_plus(
            *inputs,
            max_length=max_length,
            padding='max_length',
            truncation=True,
            return_tensors="pt"
        )
        return (
            torch.repeat_interleave(tokens['input_ids'], batch_size, 0),
            torch.repeat_interleave(tokens['attention_mask'], batch_size, 0),
            torch.repeat_interleave(tokens['token_type_ids'], batch_size, 0),
        )


    # Create the tokenizer and model
    name = "bert-base-cased-finetuned-mrpc"
    tokenizer = AutoTokenizer.from_pretrained(name)


    # Set up some example inputs
    sequence_0 = "The company HuggingFace is based in New York City"
    sequence_1 = "Apples are especially bad for your health"
    sequence_2 = "HuggingFace's headquarters are situated in Manhattan"

    paraphrase = encode(tokenizer, sequence_1, sequence_2)
    not_paraphrase = encode(tokenizer, sequence_1, sequence_1)

    def get_model():
        model = AutoModelForSequenceClassification.from_pretrained(name, torchscript=True)
        # Here we build a model with tensor-parallel layers.
        # Note: If you already have a Model class that does this, we can use that directly
        # and load the checkpoint in it.
        class ParallelSelfAttention(BertSelfAttention):
            def __init__(self, config, position_embedding_type=None):
                super().__init__(config, position_embedding_type)
                self.query = layers.ColumnParallelLinear(config.hidden_size, self.all_head_size, gather_output=False)
                self.key = layers.ColumnParallelLinear(config.hidden_size, self.all_head_size, gather_output=False)
                self.value = layers.ColumnParallelLinear(config.hidden_size, self.all_head_size, gather_output=False)
                self.num_attention_heads = self.num_attention_heads // parallel_state.get_tensor_model_parallel_size()
                self.all_head_size = self.all_head_size // parallel_state.get_tensor_model_parallel_size()

        class ParallelSelfOutput(BertSelfOutput):
            def __init__(self, config):
                super().__init__(config)
                self.dense = layers.RowParallelLinear(config.hidden_size,
                                        config.hidden_size,
                                        input_is_parallel=True)

        for layer in model.bert.encoder.layer:
            layer.attention.self = ParallelSelfAttention(model.config)
            layer.attention.output = ParallelSelfOutput(model.config)

        # Here we created a checkpoint as mentioned above. We pass sharded=False, since the checkpoint
        # we obtained is unsharded. In case you are using the checkpoint from the tensor-parallel training,
        # you can set the sharded=True, as that checkpoint will contain shards from each tp rank.
        neuronx_distributed.parallel_layers.load("bert.pt", model, sharded=False)

        # These io aliases would enable us to mark certain input tensors as state tensors. These
        # state tensors are going to be device tensors.
        io_aliases = {}
        return model, io_aliases
    
    if __name__ == "__main__":

        # Note how we are passing a function that returns a model object, which needs to be traced.
        # This is mainly done, since the model initialization needs to happen within the processes
        # that get launched internally within the parallel_model_trace.
        model = neuronx_distributed.trace.parallel_model_trace(get_model, paraphrase, tp_degree=2)

        # Once traced, we now save the trace model for future inference. This API takes care
        # of saving the checkpoint from each tensor parallel worker
        neuronx_distributed.trace.parallel_model_save(model, "tp_models")

        # We now load the saved model and will run inference against it
        model = neuronx_distributed.trace.parallel_model_load("tp_models")
        cpu_model = AutoModelForSequenceClassification.from_pretrained(name, torchscript=True)
        assert torch.argmax(model(*paraphrase)[0]) == torch.argmax(cpu_model(*paraphrase)[0])