[TVM Integration] Support TVM conversion with FP16 data type

[sxjscience]

Description

We fixed TVM integration in GluonNLP recently for fp32 dtype. However, we still do not support fp16 dtype. We should

• Revise the test to add FP16:

  gluon-nlp/tests/test_models.py

  Lines 65 to 77 in e8d4c8a

  	@pytest.mark.serial
  	@pytest.mark.seed(123)
  	@pytest.mark.parametrize('model_name',
  	['google_albert_base_v2',
  	'google_en_cased_bert_base',
  	'google_electra_small',
  	'fairseq_bart_base'])
  	@pytest.mark.parametrize('batch_size,seq_length', [(2, 4), (1, 4)])
  	@pytest.mark.parametrize('layout', ['NT', 'TN'])
  	@pytest.mark.skipif(not tvm_enabled(),
  	reason='TVM is not supported. So this test is skipped.')
  	# @pytest.mark.skip('TVM issue https://github.com/dmlc/gluon-nlp/issues/1425.')
  	def test_tvm_integration(model_name, batch_size, seq_length, layout, ctx):

• Revise the benchmark to add TVM + FP16:

  gluon-nlp/scripts/benchmarks/benchmark_utils.py

  Lines 610 to 623 in e8d4c8a

  	def compile_tvm_graph_runtime(model, model_name, layout, compute_layout,
  	batch_size, seq_length, dtype, instance_type):
  	key = (model_name, layout, compute_layout, batch_size, seq_length, dtype, instance_type)
  	if key in _TVM_RT_CACHE:
  	return _TVM_RT_CACHE[key]
  	tvm = try_import_tvm()
  	from tvm import relay
  	from tvm.contrib import graph_runtime
  	from gluonnlp.utils.tvm_utils import get_ec2_tvm_flags, update_tvm_convert_map
  	flags = get_ec2_tvm_flags()[instance_type]
  	update_tvm_convert_map()
  	token_ids_shape = (batch_size, seq_length) if layout == 'NT' else (seq_length, batch_size)
  	valid_length_shape = (batch_size,)
  	if 'bart' in model_name: