#9208: Functional SqueezeBERT model Demo

models/demos/squeezebert/README.md

@@ -0,0 +1,33 @@
+# SqueezeBERT demo
+
+Demo showcasing SqueezeBERT running on Grayskull - e150 and Wormhole - n150, n300 using ttnn.
+
+## Introduction
+SqueezeBERT is a bidirectional transformer similar to the BERT model. The key difference between the BERT architecture and the SqueezeBERT architecture is that SqueezeBERT uses grouped convolutions instead of fully-connected layers for the Q, K, V and FFN layers.
+
+
+## Details
+The entry point to  functional_squeezebert model is squeezebert_for_question_answering in `models/demos/squeezebert/tt/ttnn_functional_squeezebert.py`. The model picks up certain configs and weights from huggingface pretrained model. We have used `squeezebert/squeezebert-uncased` version from huggingface as our reference.
+
+### Sequence Size: 384
+Sequence size determines the maximum length of input sequences processed by the model, optimizing performance and compatibility. It's recommended to set the sequence_size to 384
+
+### Batch size: 8
+Batch Size determines the number of input sequences processed simultaneously during training or inference, impacting computational efficiency and memory usage. It's recommended to set the batch_size to 8
+
+## How to Run
+
+Use `pytest --disable-warnings models/demos/squeezebert/demo/demo.py::test_demo[models.demos.squeezebert.tt.ttnn_functional_squeezebert-squeezebert/squeezebert-uncased-models/demos/squeezebert/demo/input_data.json-device_params0]` to run the demo.
+
+If you wish to run the demo with a different input use `pytest --disable-warnings models/demos/squeezebert/demo/demo.py::test_demo[models.demos.squeezebert.tt.ttnn_functional_squeezebert-squeezebert/squeezebert-uncased-<path_to_input_file>-device_params0]`. This file is expected to have exactly 8 inputs.
+
+Our second demo is designed to run SQuADV2 dataset, run this with `pytest --disable-warnings models/demos/squeezebert/demo/demo.py::test_demo_squadv2[3-models.demos.squeezebert.tt.ttnn_functional_squeezebert-squeezebert/squeezebert-uncased-device_params0]`.
+
+If you wish to run for `n_iterations` samples, use `pytest --disable-warnings models/demos/squeezebert/demo/demo.py::test_demo_squadv2[<n_iterations>-models.demos.squeezebert.tt.ttnn_functional_squeezebert-squeezebert/squeezebert-uncased-device_params0]`
+
+
+## Inputs
+The demo receives inputs from respective `input_data.json` by default. To modify the inputs or specify a different path, adjust the input_path parameter in the command accordingly. It's recommended to avoid direct modifications to the input_data.json file.
+
+
+### Owner: [kkeerthana0573](https://github.com/kkeerthana0573)

models/demos/squeezebert/demo/demo.py

@@ -0,0 +1,301 @@
+# SPDX-FileCopyrightText: © 2023 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+import ttnn
+import json
+import torch
+import pytest
+import evaluate
+
+from loguru import logger
+from ttnn.model_preprocessing import *
+from models.utility_functions import (
+    profiler,
+    skip_for_wormhole_b0,
+    disable_compilation_reports,
+    disable_persistent_kernel_cache,
+)
+from ttnn.model_preprocessing import preprocess_model_parameters
+from models.demos.squeezebert.tt import ttnn_functional_squeezebert
+from models.datasets.dataset_squadv2 import squadv2_1K_samples_input, squadv2_answer_decode_batch
+
+from transformers import SqueezeBertForQuestionAnswering, pipeline, SqueezeBertTokenizer
+
+
+def load_inputs(input_path, batch):
+    with open(input_path) as f:
+        input_data = json.load(f)
+        assert len(input_data) >= batch, f"Input data needs to have at least {batch} (batch size) entries."
+
+        context = []
+        question = []
+        for i in range(batch):
+            context.append(input_data[i]["context"])
+            question.append(input_data[i]["question"])
+
+        return context, question
+
+
+def positional_ids(config, input_ids, past_key_values_length=0):
+    seq_length = input_ids.size(1)
+    position_ids = torch.arange(config.max_position_embeddings, dtype=torch.long, device=input_ids.device)
+    position_ids = position_ids.unsqueeze(0)[:, past_key_values_length : seq_length + past_key_values_length]
+    position_ids = position_ids.expand_as(input_ids)
+
+    return position_ids
+
+
+def run_squeezebert_question_and_answering_inference(
+    device,
+    use_program_cache,
+    model_name,
+    batch_size,
+    sequence_size,
+    squeezebert,
+    input_path,
+):
+    disable_persistent_kernel_cache()
+
+    hugging_face_reference_model = SqueezeBertForQuestionAnswering.from_pretrained(model_name, torchscript=False)
+    hugging_face_reference_model.eval()
+    state_dict = hugging_face_reference_model.state_dict()
+
+    tokenizer = SqueezeBertTokenizer.from_pretrained(model_name)
+    config = hugging_face_reference_model.config
+    nlp = pipeline("question-answering", model=hugging_face_reference_model, tokenizer=tokenizer)
+
+    tt_model_name = f"ttnn_{model_name}"
+
+    def convert_to_ttnn(model, name):
+        return not isinstance(model, torch.nn.Conv1d)
+
+    profiler.start(f"preprocessing_parameter")
+    parameters = preprocess_model_parameters(
+        model_name=tt_model_name,
+        initialize_model=lambda: hugging_face_reference_model,
+        convert_to_ttnn=convert_to_ttnn,
+        custom_preprocessor=squeezebert.custom_preprocessor,
+        device=device,
+    )
+    profiler.end(f"preprocessing_parameter")
+
+    context, question = load_inputs(input_path, batch_size)
+
+    preprocess_params, _, postprocess_params = nlp._sanitize_parameters()
+    preprocess_params["max_seq_len"] = sequence_size
+    inputs = nlp._args_parser({"context": context, "question": question})
+
+    preprocessed_inputs = []
+    for i in range(batch_size):
+        model_input = next(nlp.preprocess(inputs[0][i], **preprocess_params))
+
+        single_input = {
+            "example": model_input["example"],
+            "inputs": model_input,
+        }
+        preprocessed_inputs.append(single_input)
+
+    squeezebert_input = tokenizer.batch_encode_plus(
+        zip(question, context),
+        max_length=sequence_size,
+        padding="max_length",
+        truncation=True,
+        return_attention_mask=True,
+        return_token_type_ids=True,
+        return_tensors="pt",
+    )
+
+    profiler.start(f"preprocessing_input")
+    position_ids = positional_ids(config, squeezebert_input.input_ids)
+    ttnn_squeezebert_inputs = squeezebert.preprocess_inputs(
+        squeezebert_input["input_ids"],
+        squeezebert_input["token_type_ids"],
+        position_ids,
+        squeezebert_input["attention_mask"],
+        device=device,
+    )
+    profiler.end(f"preprocessing_input")
+
+    profiler.start(f"inference_time")
+    tt_output = squeezebert.squeezebert_for_question_answering(
+        config,
+        *ttnn_squeezebert_inputs,
+        state_dict=state_dict,
+        base_addr=f"transformer.",
+        parameters=parameters,
+        device=device,
+        reader_patterns_cache=None,
+    )
+    profiler.end(f"inference_time")
+
+    tt_output = ttnn.to_torch(ttnn.from_device(tt_output)).reshape(batch_size, 1, sequence_size, -1).to(torch.float32)
+
+    tt_start_logits = tt_output[..., :, 0].squeeze(1)
+    tt_end_logits = tt_output[..., :, 1].squeeze(1)
+
+    model_answers = {}
+    profiler.start("post_processing_output_to_string")
+    for i in range(batch_size):
+        tt_res = {
+            "start": tt_start_logits[i],
+            "end": tt_end_logits[i],
+            "example": preprocessed_inputs[i]["example"],
+            **preprocessed_inputs[i]["inputs"],
+        }
+        tt_answer = nlp.postprocess([tt_res], **postprocess_params)
+
+        logger.info(f"answer: {tt_answer['answer']}\n")
+        model_answers[i] = tt_answer["answer"]
+
+    profiler.end("post_processing_output_to_string")
+
+    measurements = {
+        "preprocessing_parameter": profiler.get("preprocessing_parameter"),
+        "preprocessing_input": profiler.get("preprocessing_input"),
+        "inference_time": profiler.get("inference_time"),
+        "post_processing": profiler.get("post_processing_output_to_string"),
+    }
+    logger.info(f"preprocessing_parameter: {measurements['preprocessing_parameter']} s")
+    logger.info(f"preprocessing_input: {measurements['preprocessing_input']} s")
+    logger.info(f"inference_time: {measurements['inference_time']} s")
+    logger.info(f"post_processing : {measurements['post_processing']} s")
+
+    return measurements
+
+
+def run_squeezebert_question_and_answering_inference_squad_v2(
+    device,
+    use_program_cache,
+    model_name,
+    batch_size,
+    sequence_size,
+    squeezebert,
+    n_iterations,
+):
+    disable_persistent_kernel_cache()
+
+    hugging_face_reference_model = SqueezeBertForQuestionAnswering.from_pretrained(model_name, torchscript=False)
+    hugging_face_reference_model.eval()
+    state_dict = hugging_face_reference_model.state_dict()
+
+    tokenizer = SqueezeBertTokenizer.from_pretrained(model_name)
+    config = hugging_face_reference_model.config
+    tt_model_name = ttnn_functional_squeezebert
+
+    parameters = preprocess_model_parameters(
+        model_name=tt_model_name,
+        initialize_model=lambda: hugging_face_reference_model,
+        custom_preprocessor=squeezebert.custom_preprocessor,
+        device=device,
+    )
+
+    nlp = pipeline("question-answering", model=hugging_face_reference_model, tokenizer=tokenizer)
+
+    attention_mask = True
+    token_type_ids = True
+    inputs_squadv2 = squadv2_1K_samples_input(tokenizer, sequence_size, attention_mask, token_type_ids, batch_size)
+    squad_metric = evaluate.load("squad_v2")
+
+    with torch.no_grad():
+        pred_labels = []
+        cpu_pred_labels = []
+        true_labels = []
+        i = 0
+        for batch in inputs_squadv2:
+            if i < n_iterations:
+                batch_data = batch[0]
+                curr_batch_size = batch_data["input_ids"].shape[0]
+                position_ids = positional_ids(config, batch_data.input_ids)
+                ttnn_squeezebert_inputs = squeezebert.preprocess_inputs(
+                    batch_data["input_ids"],
+                    batch_data["token_type_ids"],
+                    position_ids,
+                    batch_data["attention_mask"],
+                    device=device,
+                )
+
+                tt_output = squeezebert.squeezebert_for_question_answering(
+                    config,
+                    *ttnn_squeezebert_inputs,
+                    state_dict=state_dict,
+                    base_addr=f"transformer.",
+                    parameters=parameters,
+                    device=device,
+                    reader_patterns_cache=None,
+                )
+                tt_output = (
+                    ttnn.to_torch(ttnn.from_device(tt_output))
+                    .reshape(batch_size, 1, sequence_size, -1)
+                    .to(torch.float32)
+                )
+
+                cpu_output = hugging_face_reference_model(**batch_data)
+                references = batch[1]
+                question = batch[2]
+                context = batch[3]
+
+                cpu_predictions, tt_predictions = squadv2_answer_decode_batch(
+                    hugging_face_reference_model,
+                    tokenizer,
+                    nlp,
+                    references,
+                    cpu_output,
+                    tt_output,
+                    curr_batch_size,
+                    question,
+                    context,
+                )
+                pred_labels.extend(tt_predictions)
+                cpu_pred_labels.extend(cpu_predictions)
+                true_labels.extend(references)
+
+                del tt_output
+            i += 1
+        eval_score = squad_metric.compute(predictions=pred_labels, references=true_labels)
+        cpu_eval_score = squad_metric.compute(predictions=cpu_pred_labels, references=true_labels)
+        logger.info(f"\tTT_Eval: exact: {eval_score['exact']} --  F1: {eval_score['f1']}")
+        # logger.info(f"\tCPU_Eval: exact: {cpu_eval_score['exact']} --  F1: {cpu_eval_score['f1']}")
+
+
+@pytest.mark.parametrize("device_params", [{"l1_small_size": 16384}], indirect=True)
+@pytest.mark.parametrize(
+    "model_name, input_loc",
+    ((["squeezebert/squeezebert-uncased", "models/demos/squeezebert/demo/input_data.json"]),),
+)
+@pytest.mark.parametrize("squeezebert", [ttnn_functional_squeezebert])
+def test_demo(input_loc, model_name, squeezebert, device, use_program_cache, reset_seeds):
+    disable_persistent_kernel_cache()
+    disable_compilation_reports()
+
+    return run_squeezebert_question_and_answering_inference(
+        device=device,
+        use_program_cache=use_program_cache,
+        model_name=model_name,
+        batch_size=8,
+        sequence_size=384,
+        squeezebert=squeezebert,
+        input_path=input_loc,
+    )
+
+
+@pytest.mark.parametrize("device_params", [{"l1_small_size": 16384}], indirect=True)
+@pytest.mark.parametrize("model_name", ["squeezebert/squeezebert-uncased"])
+@pytest.mark.parametrize("squeezebert", [ttnn_functional_squeezebert])
+@pytest.mark.parametrize(
+    "n_iterations",
+    ((3),),
+)
+def test_demo_squadv2(model_name, squeezebert, n_iterations, device, use_program_cache, reset_seeds):
+    disable_persistent_kernel_cache()
+    disable_compilation_reports()
+
+    return run_squeezebert_question_and_answering_inference_squad_v2(
+        device=device,
+        use_program_cache=use_program_cache,
+        model_name=model_name,
+        batch_size=8,
+        sequence_size=384,
+        squeezebert=squeezebert,
+        n_iterations=n_iterations,
+    )