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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from itertools import product
import pytest
import torch
from audiocraft.modules.transformer import (
StreamingMultiheadAttention, StreamingTransformer, set_efficient_attention_backend)
def test_transformer_causal_streaming():
torch.manual_seed(1234)
for context, custom in product([None, 10], [False, True]):
# Test that causality and receptive fields are properly handled.
# looking at the gradients
tr = StreamingTransformer(
16, 4, 1 if context else 2,
causal=True, past_context=context, custom=custom,
dropout=0.)
steps = 20
for k in [0, 10, 15, 19]:
x = torch.randn(4, steps, 16, requires_grad=True)
y = tr(x)
y[:, k].abs().sum().backward()
if k + 1 < steps:
assert torch.allclose(x.grad[:, k + 1:], torch.tensor(0.)), x.grad[:, k + 1:].norm()
assert not torch.allclose(x.grad[:, :k + 1], torch.tensor(0.)), x.grad[:, :k + 1].norm()
if context is not None and k > context:
limit = k - context - 1
assert torch.allclose(x.grad[:, :limit],
torch.tensor(0.)), x.grad[:, :limit].norm()
# Now check that streaming gives the same result at batch eval.
x = torch.randn(4, steps, 16)
y = tr(x)
ys = []
with tr.streaming():
for k in range(steps):
chunk = x[:, k:k + 1, :]
ys.append(tr(chunk))
y_stream = torch.cat(ys, dim=1)
delta = torch.norm(y_stream - y) / torch.norm(y)
assert delta < 1e-6, delta
def test_transformer_vs_pytorch():
torch.manual_seed(1234)
# Check that in the non causal setting, we get the same result as
# PyTorch Transformer encoder.
for custom in [False, True]:
tr = StreamingTransformer(
16, 4, 2,
causal=False, custom=custom, dropout=0., positional_scale=0.)
layer = torch.nn.TransformerEncoderLayer(16, 4, dropout=0., batch_first=True)
tr_ref = torch.nn.TransformerEncoder(layer, 2)
tr.load_state_dict(tr_ref.state_dict())
x = torch.randn(4, 20, 16)
y = tr(x)
y2 = tr_ref(x)
delta = torch.norm(y2 - y) / torch.norm(y)
assert delta < 1e-6, delta
def test_streaming_api():
tr = StreamingTransformer(16, 4, 2, causal=True, dropout=0.)
tr.eval()
steps = 12
x = torch.randn(1, steps, 16)
with torch.no_grad():
with tr.streaming():
_ = tr(x[:, :1])
state = {k: v.clone() for k, v in tr.get_streaming_state().items()}
y = tr(x[:, 1:2])
tr.set_streaming_state(state)
y2 = tr(x[:, 1:2])
assert torch.allclose(y, y2), (y - y2).norm()
assert tr.flush() is None
def test_memory_efficient():
for backend in ['torch', 'xformers']:
torch.manual_seed(1234)
set_efficient_attention_backend(backend)
tr = StreamingTransformer(
16, 4, 2, custom=True, dropout=0., layer_scale=0.1)
tr_mem_efficient = StreamingTransformer(
16, 4, 2, dropout=0., memory_efficient=True, layer_scale=0.1)
tr_mem_efficient.load_state_dict(tr.state_dict())
tr.eval()
steps = 12
x = torch.randn(3, steps, 16)
with torch.no_grad():
y = tr(x)
y2 = tr_mem_efficient(x)
assert torch.allclose(y, y2), ((y - y2).norm(), backend)
def test_attention_as_float32():
torch.manual_seed(1234)
cases = [
{'custom': True},
{'custom': False},
]
for case in cases:
tr = StreamingTransformer(16, 4, 2, dropout=0., dtype=torch.bfloat16, **case)
tr_float32 = StreamingTransformer(
16, 4, 2, dropout=0., attention_as_float32=True, dtype=torch.bfloat16, **case)
if not case['custom']:
# we are not using autocast here because it doesn't really
# work as expected on CPU, so we have to manually cast the weights of the MHA.
for layer in tr_float32.layers:
layer.self_attn.mha.to(torch.float32)
tr_float32.load_state_dict(tr.state_dict())
steps = 12
x = torch.randn(3, steps, 16, dtype=torch.bfloat16)
with torch.no_grad():
y = tr(x)
y2 = tr_float32(x)
assert not torch.allclose(y, y2), (y - y2).norm()
@torch.no_grad()
def test_streaming_memory_efficient():
for backend in ['torch', 'xformers']:
torch.manual_seed(1234)
set_efficient_attention_backend(backend)
tr = StreamingTransformer(16, 4, 2, causal=True, dropout=0., custom=True)
tr_mem_efficient = StreamingTransformer(
16, 4, 2, dropout=0., memory_efficient=True, causal=True)
tr.load_state_dict(tr_mem_efficient.state_dict())
tr.eval()
tr_mem_efficient.eval()
steps = 12
x = torch.randn(3, steps, 16)
ref = tr(x)
with tr_mem_efficient.streaming():
outs = []
# frame_sizes = [2] + [1] * (steps - 2)
frame_sizes = [1] * steps
for frame_size in frame_sizes:
frame = x[:, :frame_size]
x = x[:, frame_size:]
outs.append(tr_mem_efficient(frame))
out = torch.cat(outs, dim=1)
delta = torch.norm(out - ref) / torch.norm(out)
assert delta < 1e-6, delta
def test_cross_attention():
torch.manual_seed(1234)
for norm_first in [True, False]:
m = StreamingTransformer(
16, 4, 2, cross_attention=False, norm_first=norm_first, dropout=0., custom=True)
m_cross = StreamingTransformer(
16, 4, 2, cross_attention=True, norm_first=norm_first, dropout=0., custom=True)
m_cross.load_state_dict(m.state_dict(), strict=False)
x = torch.randn(2, 5, 16)
cross_x = torch.randn(2, 3, 16)
y_ref = m(x)
y_cross_zero = m_cross(x, cross_attention_src=0 * cross_x)
# With norm_first, the two should be exactly the same,
# but with norm_first=False, we get 2 normalization in a row
# and the epsilon value leads to a tiny change.
atol = 0. if norm_first else 1e-6
print((y_ref - y_cross_zero).norm() / y_ref.norm())
assert torch.allclose(y_ref, y_cross_zero, atol=atol)
# We now expect a difference even with a generous atol of 1e-2.
y_cross = m_cross(x, cross_attention_src=cross_x)
assert not torch.allclose(y_cross, y_cross_zero, atol=1e-2)
with pytest.raises(AssertionError):
_ = m_cross(x)
_ = m(x, cross_attention_src=cross_x)
def test_cross_attention_compat():
torch.manual_seed(1234)
num_heads = 2
dim = num_heads * 64
with pytest.raises(AssertionError):
StreamingMultiheadAttention(dim, num_heads, causal=True, cross_attention=True)
cross_attn = StreamingMultiheadAttention(
dim, num_heads, dropout=0, cross_attention=True, custom=True)
ref_attn = torch.nn.MultiheadAttention(dim, num_heads, dropout=0, batch_first=True)
# We can load the regular attention state dict
# so we have compat when loading old checkpoints.
cross_attn.load_state_dict(ref_attn.state_dict())
queries = torch.randn(3, 7, dim)
keys = torch.randn(3, 9, dim)
values = torch.randn(3, 9, dim)
y = cross_attn(queries, keys, values)[0]
y_ref = ref_attn(queries, keys, values)[0]
assert torch.allclose(y, y_ref, atol=1e-7), (y - y_ref).norm() / y_ref.norm()
# Now let's check that streaming is working properly.
with cross_attn.streaming():
ys = []
for step in range(queries.shape[1]):
ys.append(cross_attn(queries[:, step: step + 1], keys, values)[0])
y_streaming = torch.cat(ys, dim=1)
assert torch.allclose(y_streaming, y, atol=1e-7)
def test_repeat_kv():
torch.manual_seed(1234)
num_heads = 8
kv_repeat = 4
dim = num_heads * 64
with pytest.raises(AssertionError):
mha = StreamingMultiheadAttention(
dim, num_heads, causal=True, kv_repeat=kv_repeat, cross_attention=True)
mha = StreamingMultiheadAttention(
dim, num_heads, causal=True, kv_repeat=kv_repeat)
mha = StreamingMultiheadAttention(
dim, num_heads, causal=True, kv_repeat=kv_repeat, custom=True)
x = torch.randn(4, 18, dim)
y = mha(x, x, x)[0]
assert x.shape == y.shape
def test_qk_layer_norm():
torch.manual_seed(1234)
tr = StreamingTransformer(
16, 4, 2, custom=True, dropout=0., qk_layer_norm=True, bias_attn=False)
steps = 12
x = torch.randn(3, steps, 16)
y = tr(x)
tr = StreamingTransformer(
16, 4, 2, custom=True, dropout=0., qk_layer_norm=True, cross_attention=True)
z = torch.randn(3, 21, 16)
y = tr(x, cross_attention_src=z)
assert y.shape == x.shape
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