fine tune with shark (#211)

tank/README.md

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+To run the fine tuning example, from the root SHARK directory, run:
+
+```shell
+IMPORTER=1 ./setup_venv.sh
+source shark.venv/bin/activate
+pip install jupyter tf-models-nightly tf-datasets
+jupyter-notebook
+```
+if running from a google vm, you can view jupyter notebooks on your local system with:
+```shell
+gcloud compute ssh <YOUR_INSTANCE_DETAILS> --ssh-flag="-N -L localhost:8888:localhost:8888"
+```
+

tank/bert_fine_tuning/bert_fine_tune_tf.py

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+import numpy as np
+
+from iree import runtime as ireert
+from iree.tf.support import module_utils
+from iree.compiler import tf as tfc
+from iree.compiler import compile_str
+
+import tensorflow as tf
+
+try:
+    import tensorflow_datasets as tfds
+    import tensorflow_models as tfm
+    from official.nlp.modeling import layers
+    from official.nlp.modeling import networks
+    from official.nlp.modeling.models import bert_classifier
+except ModuleNotFoundError:
+    print(
+        "tensorflow models or datasets not found please run the following command with your virtual env active:\npip install tf-models-nightly tf-datasets"
+    )
+import json
+import time
+import os
+
+gs_folder_bert = "gs://cloud-tpu-checkpoints/bert/v3/uncased_L-12_H-768_A-12"
+tf.io.gfile.listdir(gs_folder_bert)
+vocab_size = 100
+NUM_CLASSES = 2
+SEQUENCE_LENGTH = 128
+BATCH_SIZE = 1
+# Create a set of 2-dimensional inputs
+bert_input = [
+    tf.TensorSpec(shape=[BATCH_SIZE, SEQUENCE_LENGTH], dtype=tf.int32),
+    tf.TensorSpec(shape=[BATCH_SIZE, SEQUENCE_LENGTH], dtype=tf.int32),
+    tf.TensorSpec(shape=[BATCH_SIZE, SEQUENCE_LENGTH], dtype=tf.int32),
+]
+
+
+class BertModule(tf.Module):
+    def __init__(self):
+        super(BertModule, self).__init__()
+        dict_outputs = False
+
+        bert_config_file = os.path.join(gs_folder_bert, "bert_config.json")
+
+        config_dict = json.loads(tf.io.gfile.GFile(bert_config_file).read())
+        encoder_config = tfm.nlp.encoders.EncoderConfig(
+            {"type": "bert", "bert": config_dict}
+        )
+        bert_encoder = tfm.nlp.encoders.build_encoder(encoder_config)
+
+        # Create a BERT trainer with the created network.
+        bert_trainer_model = bert_classifier.BertClassifier(
+            bert_encoder, num_classes=NUM_CLASSES
+        )
+        bert_trainer_model.summary()
+        checkpoint = tf.train.Checkpoint(encoder=bert_encoder)
+        checkpoint.read(
+            os.path.join(gs_folder_bert, "bert_model.ckpt")
+        ).assert_consumed()
+
+        # Invoke the trainer model on the inputs. This causes the layer to be built.
+        self.m = bert_trainer_model
+        self.m.predict = lambda x: self.m.call(x, training=False)
+        self.predict = tf.function(input_signature=[bert_input])(
+            self.m.predict
+        )
+        self.m.learn = lambda x, y: self.m.call(x, training=False)
+        self.loss = tf.keras.losses.SparseCategoricalCrossentropy()
+        self.optimizer = tf.keras.optimizers.SGD(learning_rate=1e-2)
+
+    @tf.function(
+        input_signature=[
+            bert_input,  # inputs
+            tf.TensorSpec(shape=[BATCH_SIZE], dtype=tf.int32),  # labels
+        ]
+    )
+    def learn(self, inputs, labels):
+        with tf.GradientTape() as tape:
+            # Capture the gradients from forward prop...
+            probs = self.m.call(inputs, training=True)
+            loss = self.loss(labels, probs)
+
+        # ...and use them to update the model's weights.
+        variables = self.m.trainable_variables
+        gradients = tape.gradient(loss, variables)
+        self.optimizer.apply_gradients(zip(gradients, variables))
+        return loss
+
+
+if __name__ == "__main__":
+    glue, info = tfds.load("glue/mrpc", with_info=True, batch_size=BATCH_SIZE)
+
+    tokenizer = tfm.nlp.layers.FastWordpieceBertTokenizer(
+        vocab_file=os.path.join(gs_folder_bert, "vocab.txt"), lower_case=True
+    )
+
+    max_seq_length = SEQUENCE_LENGTH
+
+    packer = tfm.nlp.layers.BertPackInputs(
+        seq_length=max_seq_length,
+        special_tokens_dict=tokenizer.get_special_tokens_dict(),
+    )
+
+    class BertInputProcessor(tf.keras.layers.Layer):
+        def __init__(self, tokenizer, packer):
+            super().__init__()
+            self.tokenizer = tokenizer
+            self.packer = packer
+
+        def call(self, inputs):
+            tok1 = self.tokenizer(inputs["sentence1"])
+            tok2 = self.tokenizer(inputs["sentence2"])
+
+            packed = self.packer([tok1, tok2])
+
+            if "label" in inputs:
+                return packed, inputs["label"]
+            else:
+                return packed
+
+    bert_inputs_processor = BertInputProcessor(tokenizer, packer)
+    glue_train = glue["train"].map(bert_inputs_processor).prefetch(1)
+    glue_validation = glue["validation"].map(bert_inputs_processor).prefetch(1)
+    glue_test = glue["test"].map(bert_inputs_processor).prefetch(1)
+
+    # base tensorflow model
+    bert_model = BertModule()
+
+    # Compile the model using IREE
+    compiler_module = tfc.compile_module(
+        bert_model, exported_names=["learn"], import_only=True
+    )
+
+    # choose from dylib-llvm-aot or cuda
+    backend = "dylib-llvm-aot"
+    if backend == "dylib-llvm-aot":
+        args = [
+            "--iree-llvm-target-cpu-features=host",
+            "--iree-mhlo-demote-i64-to-i32=false",
+            "--iree-flow-demote-i64-to-i32",
+        ]
+        backend_config = "dylib"
+
+    else:
+        backend_config = "cuda"
+        args = [
+            "--iree-cuda-llvm-target-arch=sm_80",
+            "--iree-hal-cuda-disable-loop-nounroll-wa",
+            "--iree-enable-fusion-with-reduction-ops",
+        ]
+
+    flatbuffer_blob = compile_str(
+        compiler_module,
+        target_backends=[backend],
+        extra_args=args,
+        input_type="mhlo",
+    )
+
+    # Save module as MLIR file in a directory
+    vm_module = ireert.VmModule.from_flatbuffer(flatbuffer_blob)
+    tracer = ireert.Tracer(os.getcwd())
+    config = ireert.Config("local-sync", tracer)
+    ctx = ireert.SystemContext(config=config)
+    ctx.add_vm_module(vm_module)
+    BertCompiled = ctx.modules.module
+
+    # compare output losses:
+
+    iterations = 10
+    for i in range(iterations):
+        example_inputs, example_labels = next(iter(glue_train))
+        example_labels = tf.cast(example_labels, tf.int32)
+        example_inputs = [value for key, value in example_inputs.items()]
+
+        # iree version
+        iree_loss = BertCompiled.learn(
+            example_inputs, example_labels
+        ).to_host()
+
+        # base tensorflow
+        tf_loss = np.array(bert_model.learn(example_inputs, example_labels))
+        print(np.allclose(iree_loss, tf_loss))