waytrue17 commented on a change in pull request #20048:
URL: https://github.com/apache/incubator-mxnet/pull/20048#discussion_r598031213
##########
File path: tests/python-pytest/onnx/test_onnxruntime.py
##########
@@ -988,3 +988,170 @@ def test_ernie_inference_onnxruntime(tmp_path,
model_name):
finally:
shutil.rmtree(tmp_path)
+
+
+@with_seed()
[email protected]('model_name', ['transformer_en_de_512'])
+def test_transformer_pretrained_inference_onnxruntime(tmp_path, model_name):
+ tmp_path = str(tmp_path)
+ try:
+ import gluonnlp as nlp
+ dataset = 'WMT2014'
+ ctx = mx.cpu(0)
+ model, _, _ = nlp.model.get_model(
+ name=model_name,
+ ctx=ctx,
+ pretrained=True,
+ dataset_name=dataset)
+
+ model.hybridize(static_alloc=False)
+
+ batch = 7
+ seq_length = 16
+ C_in = 512
Review comment:
What are C_in and C_out? Should we also test when `C_in != C_out`?
##########
File path: tests/python-pytest/onnx/test_onnxruntime.py
##########
@@ -988,3 +988,170 @@ def test_ernie_inference_onnxruntime(tmp_path,
model_name):
finally:
shutil.rmtree(tmp_path)
+
+
+@with_seed()
[email protected]('model_name', ['transformer_en_de_512'])
+def test_transformer_pretrained_inference_onnxruntime(tmp_path, model_name):
+ tmp_path = str(tmp_path)
+ try:
+ import gluonnlp as nlp
+ dataset = 'WMT2014'
+ ctx = mx.cpu(0)
+ model, _, _ = nlp.model.get_model(
+ name=model_name,
+ ctx=ctx,
+ pretrained=True,
+ dataset_name=dataset)
+
+ model.hybridize(static_alloc=False)
+
+ batch = 7
+ seq_length = 16
+ C_in = 512
+ C_out = 512
+ src = mx.nd.random.uniform(0, 36794, shape=(batch, seq_length),
dtype='float32')
Review comment:
Curious, does not `src` need to be int type?
##########
File path: tests/python-pytest/onnx/test_onnxruntime.py
##########
@@ -988,3 +988,170 @@ def test_ernie_inference_onnxruntime(tmp_path,
model_name):
finally:
shutil.rmtree(tmp_path)
+
+
+@with_seed()
[email protected]('model_name', ['transformer_en_de_512'])
+def test_transformer_pretrained_inference_onnxruntime(tmp_path, model_name):
+ tmp_path = str(tmp_path)
+ try:
+ import gluonnlp as nlp
+ dataset = 'WMT2014'
+ ctx = mx.cpu(0)
+ model, _, _ = nlp.model.get_model(
+ name=model_name,
+ ctx=ctx,
+ pretrained=True,
+ dataset_name=dataset)
+
+ model.hybridize(static_alloc=False)
+
+ batch = 7
+ seq_length = 16
+ C_in = 512
+ C_out = 512
+ src = mx.nd.random.uniform(0, 36794, shape=(batch, seq_length),
dtype='float32')
+ step_input = mx.nd.random.uniform(0, 36794, shape=(batch,),
dtype='float32')
+ src_valid_length = mx.nd.array([seq_length] * batch, dtype='float32')
+
+ encoder_outputs, encoder_additional_outputs = model.encode(src,
+
valid_length=src_valid_length)
+
+ decoder_states =
model.decoder.init_state_from_encoder(encoder_outputs, src_valid_length)
+
+ step_output, states, additional_outputs =
model.decode_step(step_input, decoder_states)
+
+ # skip export of 'decoder' as it's used for training only
+ for component in ['encoder', 'one_step_ahead_decoder', 'src_embed',
'tgt_embed',
+ 'tgt_proj']:
+
+ prefix = "%s/%s" %(tmp_path, component)
+ component = getattr(model, component)
+ component.export(prefix)
+ sym_file = "%s-symbol.json" % prefix
+ params_file = "%s-0000.params" % prefix
+ onnx_file = "%s.onnx" % prefix
+
+ def export_to_onnx(prefix, input_shapes, input_types, **kwargs):
+ sym_file = "%s-symbol.json" % prefix
+ params_file = "%s-0000.params" % prefix
+ onnx_file = "%s.onnx" % prefix
+ return mx.contrib.onnx.export_model(sym_file, params_file,
input_shapes, input_types,
+ onnx_file, **kwargs)
+
+ def onnx_runtime_predict(onnx_file, onnx_inputs):
+ ses_opt = onnxruntime.SessionOptions()
+ ses_opt.log_severity_level = 3
+ session = onnxruntime.InferenceSession(onnx_file, ses_opt)
+ input_dict = dict((session.get_inputs()[i].name,
onnx_inputs[i].asnumpy())
+ for i in range(len(onnx_inputs)))
+ return session.run(None, input_dict)
+
+ def verify_encoder():
+ inputs = mx.nd.random.uniform(-1, 1, shape=(batch, seq_length,
C_in), dtype='float32')
+ valid_length = mx.nd.array([seq_length] * batch, dtype='float32')
+ pred = model.encoder(inputs, valid_length=valid_length)
+
+ prefix = "%s/encoder" %tmp_path
+ input_shapes = [(batch, seq_length, C_in), (batch,)]
+ input_types = [np.float32, np.float32]
+ onnx_file = export_to_onnx(prefix, input_shapes, input_types)
+ onnx_inputs = [inputs, valid_length]
+ pred_onx = onnx_runtime_predict(onnx_file, onnx_inputs)
+
+ assert_almost_equal(pred[0], pred_onx[0])
+
+ def verify_src_embed():
+ src = mx.nd.random.uniform(0, 36794, shape=(batch, seq_length),
dtype='float32')
+ pred = model.src_embed(src)
+
+ prefix = "%s/src_embed" %tmp_path
+ input_shapes = [(batch, seq_length)]
+ input_types = [np.float32]
+ onnx_file = export_to_onnx(prefix, input_shapes, input_types)
+ onnx_inputs = [src]
+ pred_onx = onnx_runtime_predict(onnx_file, onnx_inputs)
+
+ assert_almost_equal(pred, pred_onx[0])
+
+ def verify_tgt_embed():
+ tgt = mx.nd.random.uniform(0, 36794, shape=(batch, seq_length),
dtype='float32')
+ pred = model.tgt_embed(tgt)
+
+ prefix = "%s/tgt_embed" %tmp_path
+ input_shapes = [(batch, seq_length)]
+ input_types = [np.float32]
+ onnx_file = export_to_onnx(prefix, input_shapes, input_types)
+ onnx_inputs = [tgt]
+ pred_onx = onnx_runtime_predict(onnx_file, onnx_inputs)
+
+ assert_almost_equal(pred, pred_onx[0])
+
+ def verify_tgt_proj():
+ decoder_out = mx.nd.random.uniform(0, 512, shape=(batch,
seq_length, C_out),
+ dtype='float32')
+ pred = model.tgt_proj(decoder_out)
+
+ prefix = "%s/tgt_proj" %tmp_path
+ input_shapes = [(batch, seq_length, C_out)]
+ input_types = [np.float32]
+ onnx_file = export_to_onnx(prefix, input_shapes, input_types)
+ onnx_inputs = [decoder_out]
+ pred_onx = onnx_runtime_predict(onnx_file, onnx_inputs)
+
+ assert_almost_equal(pred, pred_onx[0], rtol=1.e-04, atol=1.5e-03)
+
+ def verify_one_step_ahead_decoder():
+ prefix = "%s/one_step_ahead_decoder" %tmp_path
+
+ # the input data order
+ perm = [2, 0, 1]
Review comment:
Could we put the correct order when instantiating the list instead of
using perm?
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