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update test functions for emformer.

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This commit is contained in:
yaozengwei 2022-04-14 16:41:52 +08:00
parent 4130892971
commit 524f3aa015
2 changed files with 162 additions and 31 deletions
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9
egs/librispeech/ASR/emformer_pruned_transducer_stateless/emformer.py
View File

@ -85,8 +85,6 @@ class EmformerAttention(nn.Module):
Embedding dimension.
nhead (int):
Number of attention heads in each Emformer layer.
dropout (float, optional):
Dropout probability. (Default: 0.0)
weight_init_gain (float or None, optional):
Scale factor to apply when initializing attention
module parameters. (Default: ``None``)
@ -100,7 +98,6 @@ class EmformerAttention(nn.Module):
self,
embed_dim: int,
nhead: int,
dropout: float = 0.0,
weight_init_gain: Optional[float] = None,
tanh_on_mem: bool = False,
negative_inf: float = -1e8,
@ -115,7 +112,6 @@ class EmformerAttention(nn.Module):
self.embed_dim = embed_dim
self.nhead = nhead
self.dropout = dropout
self.tanh_on_mem = tanh_on_mem
self.negative_inf = negative_inf
@ -183,9 +179,7 @@ class EmformerAttention(nn.Module):
attention_probs = nn.functional.softmax(
attention_weights_float, dim=-1
).type_as(attention_weights)
# attention_probs = nn.functional.dropout(
# attention_probs, p=float(self.dropout), training=self.training
# )
return attention_probs
def _forward_impl(
@ -512,7 +506,6 @@ class EmformerLayer(nn.Module):
self.attention = EmformerAttention(
embed_dim=d_model,
nhead=nhead,
dropout=dropout,
weight_init_gain=weight_init_gain,
tanh_on_mem=tanh_on_mem,
negative_inf=negative_inf,

184
egs/librispeech/ASR/emformer_pruned_transducer_stateless/test_emformer.py
View File

@ -362,8 +362,9 @@ def test_emformer_attention_forward_infer_consistency():
left_context_length=L,
right_context_length=R,
max_memory_size=M,
dropout=0.0,
dropout=0.1,
)
encoder.eval()
encoder_layer = encoder.emformer_layers[0]
x = torch.randn(U + R, 1, D)
@ -415,12 +416,15 @@ def test_emformer_attention_forward_infer_consistency():
chunk_memory,
state,
)
infer_output_utterance = infer_output_right_context_utterance[
infer_output_chunk = infer_output_right_context_utterance[
chunk_right_context.size(0) : # noqa
]
print(
infer_output_utterance
- forward_output_utterance[start_idx:end_idx]
forward_output_chunk = forward_output_utterance[start_idx:end_idx]
assert torch.allclose(
infer_output_chunk,
forward_output_chunk,
atol=1e-6,
rtol=0.0,
)
@ -444,8 +448,9 @@ def test_emformer_layer_forward_infer_consistency():
left_context_length=L,
right_context_length=R,
max_memory_size=M,
dropout=0.0,
dropout=0.1,
)
encoder.eval()
encoder_layer = encoder.emformer_layers[0]
x = torch.randn(U + R, 1, D)
@ -485,7 +490,7 @@ def test_emformer_layer_forward_infer_consistency():
else torch.empty(0).to(dtype=x.dtype, device=x.device)
)
(
infer_output_utterance,
infer_output_chunk,
infer_right_context,
infer_output_memory,
state,
@ -496,9 +501,12 @@ def test_emformer_layer_forward_infer_consistency():
chunk_memory,
state,
)
print(
infer_output_utterance
- forward_output_utterance[start_idx:end_idx]
forward_output_chunk = forward_output_utterance[start_idx:end_idx]
assert torch.allclose(
infer_output_chunk,
forward_output_chunk,
atol=1e-5,
rtol=0.0,
)
@ -522,8 +530,9 @@ def test_emformer_encoder_forward_infer_consistency():
left_context_length=L,
right_context_length=R,
max_memory_size=M,
dropout=0.0,
dropout=0.1,
)
encoder.eval()
x = torch.randn(U + R, 1, D)
lengths = torch.tensor([U + R])
@ -537,23 +546,152 @@ def test_emformer_encoder_forward_infer_consistency():
chunk = x[start_idx : end_idx + R] # noqa
chunk_right_context = x[end_idx : end_idx + R] # noqa
chunk_length = torch.tensor([chunk_length])
infer_output, infer_output_lengths, states = encoder.infer(
infer_output_chunk, infer_output_lengths, states = encoder.infer(
chunk,
chunk_length,
states,
)
print(infer_output - forward_output[start_idx:end_idx])
forward_output_chunk = forward_output[start_idx:end_idx]
assert torch.allclose(
infer_output_chunk,
forward_output_chunk,
atol=1e-5,
rtol=0.0,
)
def test_emformer_infer_batch_single_consistency():
"""Test consistency of cached states and output logits between single
utterance inference and batch inference."""
from emformer import Emformer
num_features = 80
output_dim = 1000
chunk_length = 8
num_chunks = 3
U = num_chunks * chunk_length
L, R = 128, 4
B, D = 2, 256
num_encoder_layers = 2
for use_memory in [True, False]:
if use_memory:
M = 3
else:
M = 0
model = Emformer(
num_features=num_features,
output_dim=output_dim,
chunk_length=chunk_length,
subsampling_factor=4,
d_model=D,
num_encoder_layers=num_encoder_layers,
left_context_length=L,
right_context_length=R,
max_memory_size=M,
vgg_frontend=False,
)
model.eval()
def save_states(states):
saved_states = []
for layer_idx in range(len(states)):
layer_state = []
layer_state.append(states[layer_idx][0].clone()) # memory
layer_state.append(
states[layer_idx][1].clone()
) # left_context_key
layer_state.append(
states[layer_idx][2].clone()
) # left_context_val
layer_state.append(states[layer_idx][3].clone()) # past_length
saved_states.append(layer_state)
return saved_states
def assert_states_equal(saved_states, states, sample_idx):
for layer_idx in range(len(saved_states)):
# assert eqaul memory
assert torch.allclose(
states[layer_idx][0],
saved_states[layer_idx][0][
:, sample_idx : sample_idx + 1 # noqa
],
atol=1e-5,
rtol=0.0,
)
# assert equal left_context_key
assert torch.allclose(
states[layer_idx][1],
saved_states[layer_idx][1][
:, sample_idx : sample_idx + 1 # noqa
],
atol=1e-5,
rtol=0.0,
)
# assert equal left_context_val
assert torch.allclose(
states[layer_idx][2],
saved_states[layer_idx][2][
:, sample_idx : sample_idx + 1 # noqa
],
atol=1e-5,
rtol=0.0,
)
# assert eqaul past_length
assert torch.equal(
states[layer_idx][3],
saved_states[layer_idx][3][
:, sample_idx : sample_idx + 1 # noqa
],
)
x = torch.randn(B, U + R + 3, num_features)
batch_logits = []
batch_states = []
states = None
for chunk_idx in range(num_chunks):
start_idx = chunk_idx * chunk_length
end_idx = start_idx + chunk_length
chunk = x[:, start_idx : end_idx + R + 3] # noqa
lengths = torch.tensor([chunk_length + R + 3]).expand(B)
logits, output_lengths, states = model.infer(chunk, lengths, states)
batch_logits.append(logits)
batch_states.append(save_states(states))
batch_logits = torch.cat(batch_logits, dim=1)
single_logits = []
for sample_idx in range(B):
sample = x[sample_idx : sample_idx + 1] # noqa
chunk_logits = []
states = None
for chunk_idx in range(num_chunks):
start_idx = chunk_idx * chunk_length
end_idx = start_idx + chunk_length
chunk = sample[:, start_idx : end_idx + R + 3] # noqa
lengths = torch.tensor([chunk_length + R + 3])
logits, output_lengths, states = model.infer(
chunk, lengths, states
)
chunk_logits.append(logits)
assert_states_equal(batch_states[chunk_idx], states, sample_idx)
chunk_logits = torch.cat(chunk_logits, dim=1)
single_logits.append(chunk_logits)
single_logits = torch.cat(single_logits, dim=0)
assert torch.allclose(batch_logits, single_logits, atol=1e-5, rtol=0.0)
if __name__ == "__main__":
# test_emformer_attention_forward()
# test_emformer_attention_infer()
# test_emformer_layer_forward()
# test_emformer_layer_infer()
# test_emformer_encoder_forward()
# test_emformer_encoder_infer()
# test_emformer_forward()
# test_emformer_infer()
# test_emformer_attention_forward_infer_consistency()
# test_emformer_layer_forward_infer_consistency()
test_emformer_attention_forward()
test_emformer_attention_infer()
test_emformer_layer_forward()
test_emformer_layer_infer()
test_emformer_encoder_forward()
test_emformer_encoder_infer()
test_emformer_forward()
test_emformer_infer()
test_emformer_attention_forward_infer_consistency()
test_emformer_layer_forward_infer_consistency()
test_emformer_encoder_forward_infer_consistency()
test_emformer_infer_batch_single_consistency()