jwfromm commented on a change in pull request #4825: [Frontend][ONNX] LSTM
Support
URL: https://github.com/apache/incubator-tvm/pull/4825#discussion_r376137993
##########
File path: tests/python/frontend/onnx/test_forward.py
##########
@@ -1962,6 +1962,126 @@ def test_pooling():
auto_pad='SAME_UPPER')
+def verify_lstm(seq_length,
+ batch_size,
+ input_size,
+ hidden_size,
+ use_bias=False,
+ activations=None,
+ alphas=None,
+ betas=None):
+ x_np = np.random.uniform(size=(seq_length, batch_size,
input_size)).astype('float32')
+ w_np = np.random.uniform(size=(1, 4 * hidden_size,
input_size)).astype('float32')
+ r_np = np.random.uniform(size=(1, 4 * hidden_size,
hidden_size)).astype('float32')
+ input_names = ["X", "W", "R"]
+ input_tensors = [
+ helper.make_tensor_value_info("X", TensorProto.FLOAT,
list(x_np.shape)),
+ helper.make_tensor_value_info("W", TensorProto.FLOAT,
list(w_np.shape)),
+ helper.make_tensor_value_info("R", TensorProto.FLOAT, list(r_np.shape))
+ ]
+ input_values = [x_np, w_np, r_np]
+ if use_bias:
+ b_np = np.random.uniform(size=(1, 8 * hidden_size)).astype('float32')
+ input_names.append("B")
+ input_tensors.append(
+ helper.make_tensor_value_info("B", TensorProto.FLOAT, [1, 8 *
hidden_size]))
+ input_values.append(b_np)
+
+ Y_shape = [seq_length, 1, batch_size, hidden_size]
+ Y_h_shape = [1, batch_size, hidden_size]
+ Y_c_shape = [1, batch_size, hidden_size]
+
+ if activations is None:
+ lstm_node = helper.make_node(
+ 'LSTM', inputs=input_names, outputs=["Y", "Y_h", "Y_c"],
hidden_size=hidden_size)
+ elif alphas is None:
+ lstm_node = helper.make_node(
+ 'LSTM',
+ inputs=input_names,
+ outputs=["Y", "Y_h", "Y_c"],
+ hidden_size=hidden_size,
+ activations=activations)
+ else:
+ lstm_node = helper.make_node(
+ 'LSTM',
+ inputs=input_names,
+ outputs=["Y", "Y_h", "Y_c"],
+ hidden_size=hidden_size,
+ activations=activations,
+ activation_alpha=alphas,
+ activation_beta=betas)
+
+ graph = helper.make_graph([lstm_node],
+ "lstm_test",
+ inputs=input_tensors,
+ outputs=[
+ helper.make_tensor_value_info("Y",
TensorProto.FLOAT,
+ list(Y_shape)),
+ helper.make_tensor_value_info("Y_h",
TensorProto.FLOAT,
+
list(Y_h_shape)),
+ helper.make_tensor_value_info("Y_c",
TensorProto.FLOAT,
+
list(Y_c_shape))
+ ])
+
+ model = helper.make_model(graph, producer_name='lstm_test')
+
+ for target, ctx in ctx_list():
+ onnx_out = get_onnxruntime_output(model, input_values, 'float32')
+ tvm_out = get_tvm_output(
+ model,
+ input_values,
+ target,
+ ctx, [Y_shape, Y_h_shape, Y_c_shape],
+ output_dtype=['float32', 'float32', 'float32'])
+ for o_out, t_out in zip(onnx_out, tvm_out):
+ tvm.testing.assert_allclose(o_out, t_out, rtol=5e-3, atol=5e-3)
+
+
+def test_lstm():
Review comment:
New tests for initial states and peephole weights are added. Glad you
pointed this out since both those cases had some small bugs.
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