[GitHub] [incubator-tvm] sxjscience edited a comment on pull request #6699: [Frontend][Relay] Fix MXNet frontend to support NLP backbones in GluonNLP

[GitBox <gi...@apache.org>]

sxjscience edited a comment on pull request #6699:
URL: https://github.com/apache/incubator-tvm/pull/6699#issuecomment-711111989


   I've verified the TVM integration with 5 NLP backbones in GluonNLP: BERT, ALBERT, ELECTRA, RoBERTA, and BART
   
   ```python
   import mxnet as mx
   import numpy as np
   import gluonnlp
   from gluonnlp.models import get_backbone
   import numpy.testing as npt
   import tvm
   from tvm import relay
   import tvm.contrib.graph_runtime as runtime
   
   
   mx.npx.set_np()
   
   instance_info = {
       'g4': {'target': "cuda -model=t4", 'use_gpu': True},
       'c4': {'target': 'llvm -mcpu=core-avx2 -libs=cblas', 'use_gpu': False},
       'c5': {'target': 'llvm -mcpu=skylake-avx512 -libs=cblas', 'use_gpu': False},
       'p3': {'target': 'cuda -model=v100', 'use_gpu': True}
   }
   
   
   def test_backbone(model_name, batch_size=2, seq_length=128, instance='g4',
                     required_pass=None, opt_level=3):
       if required_pass is None:
           required_pass = ["FastMath"]
       model_cls, cfg, tokenizer, backbone_param_path, _ = get_backbone(model_name)
       model = model_cls.from_cfg(cfg)
       model.load_parameters(backbone_param_path)
       model.hybridize()
       token_ids = mx.np.random.randint(0, cfg.MODEL.vocab_size, (batch_size, seq_length), dtype=np.int32)
       token_types = mx.np.random.randint(0, 2, (batch_size, seq_length), dtype=np.int32)
       valid_length = mx.np.random.randint(seq_length // 2, seq_length, (batch_size,), dtype=np.int32)
       if 'bart' in model_name:
           mx_out = model(token_ids, valid_length, token_ids, valid_length)
           shape_dict = {
               'data0': token_ids.shape,
               'data1': valid_length.shape,
               'data2': token_ids.shape,
               'data3': valid_length.shape,
           }
           dtype_dict = {
               'data0': token_ids.dtype.name,
               'data1': valid_length.dtype.name,
               'data2': token_ids.dtype.name,
               'data3': valid_length.dtype.name,
           }
       elif 'roberta' in model_name or 'xlmr' in model_name:
           mx_out = model(token_ids, valid_length)
           shape_dict = {
               'data0': token_ids.shape,
               'data1': valid_length.shape,
           }
           dtype_dict = {
               'data0': token_ids.dtype.name,
               'data1': valid_length.dtype.name,
           }
       else:
           mx_out = model(token_ids, token_types, valid_length)
           shape_dict = {
               'data0': token_ids.shape,
               'data1': token_types.shape,
               'data2': valid_length.shape
           }
           dtype_dict = {
               'data0': token_ids.dtype.name,
               'data1': token_types.dtype.name,
               'data2': valid_length.dtype.name
           }
       sym = model._cached_graph[1]
       params = {}
       for k, v in model.collect_params().items():
           params[v._var_name] = tvm.nd.array(v.data().asnumpy())
       mod, params = relay.frontend.from_mxnet(sym, shape=shape_dict, dtype=dtype_dict, arg_params=params)
       target = instance_info[instance]['target']
       use_gpu = instance_info[instance]['use_gpu']
       with relay.build_config(opt_level=opt_level, required_pass=required_pass):
           graph, lib, cparams = relay.build(mod, target, params=params)
       if use_gpu:
           ctx = tvm.gpu()
       else:
           ctx = tvm.cpu()
       rt = runtime.create(graph, lib, ctx)
       rt.set_input(**cparams)
       if 'bart' in model_name:
           rt.set_input(data0=token_ids, data1=valid_length, data2=token_ids, data3=valid_length)
       elif 'roberta' in model_name:
           rt.set_input(data0=token_ids, data1=valid_length)
       else:
           rt.set_input(data0=token_ids, data1=token_types, data2=valid_length)
       rt.run()
       for i in range(rt.get_num_outputs()):
           out = rt.get_output(i)
           if rt.get_num_outputs() == 1:
               mx_out_gt = mx_out.asnumpy()
           else:
               mx_out_gt = mx_out[i].asnumpy()
           if 'mobilebert' in model_name and len(out.shape) == 3:
               npt.assert_allclose(out.asnumpy()[:, 1:, :], mx_out[i].asnumpy()[:, 1:, :],
                                   rtol=6e-2, atol=6e-2)
           else:
               npt.assert_allclose(out.asnumpy(), mx_out_gt, rtol=6e-2, atol=6e-2)
   # test_backbone('google_en_cased_bert_base', instance='g4')
   test_model_names = ['google_albert_base_v2',
                       'google_en_cased_bert_base',
                       'google_electra_small',
                       'fairseq_roberta_base',
                       'fairseq_bart_base']
   for model_name in test_model_names:
       test_backbone(model_name, instance='g4')
   
   ```


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