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import torch
def do_test():
try:
import causal_conv1d # noqa: F401
import tilelang # noqa: F401
causal_conv1d_fn = causal_conv1d.causal_conv1d_fn # noqa: F841
causal_conv1d_update = causal_conv1d.causal_conv1d_update # noqa: F841
return True
except Exception:
return False
@pytest.mark.skipif(not do_test(), reason='tilelang or causal_conv1d is not available')
class TestCausalConv1dUpdate:
@pytest.fixture
def device(self):
yield 'cuda'
@pytest.fixture
def batch(self):
yield 128
@pytest.fixture
def hidden_size(self):
yield 2048
@pytest.fixture
def width(self):
yield 4
@pytest.fixture(params=[1, 4])
def seqlen(self, request):
yield request.param
@pytest.fixture
def x(self, batch, hidden_size, seqlen, device):
yield torch.randn(batch, hidden_size, seqlen, device=device)
@pytest.fixture
def weight(self, hidden_size, width, device):
yield torch.randn(hidden_size, width, device=device)
@pytest.fixture
def bias(self, hidden_size, device):
yield torch.randn(hidden_size, device=device)
@pytest.fixture(params=[True, False])
def cache_seqlens(self, request, batch, device):
if request.param:
yield torch.randint(0, 4096, (batch, ), dtype=torch.int32, device=device)
else:
yield None
@pytest.fixture(params=[True, False])
def conv_state_indices(self, request, batch, device):
if request.param:
conv_state_indices = batch * 2 - 1 - torch.arange(0, batch * 2, 2, device=device)
yield conv_state_indices.to(torch.int32)
else:
yield None
@pytest.fixture
def conv_state(self, batch, hidden_size, width, device, conv_state_indices):
if conv_state_indices is not None:
conv_state = torch.randn(batch * 4, hidden_size, width, device=device)
conv_state = conv_state[::2]
else:
conv_state = torch.randn(batch, hidden_size, width, device=device)
yield conv_state
@pytest.fixture(params=[None, 'silu'])
def activation(self, request):
yield request.param
def test_causal_conv1d_update(self, x, conv_state, weight, bias, activation, cache_seqlens, conv_state_indices):
from causal_conv1d import causal_conv1d_update as causal_conv1d_update_gt
from lmdeploy.pytorch.kernels.cuda.causal_conv1d import causal_conv1d_update
conv_state_clone = conv_state.clone()
out = causal_conv1d_update(x=x,
conv_state=conv_state_clone,
weight=weight,
bias=bias,
activation=activation,
cache_seqlens=cache_seqlens,
conv_state_indices=conv_state_indices)
out_gt = causal_conv1d_update_gt(x=x,
conv_state=conv_state,
weight=weight,
bias=bias,
activation=activation,
cache_seqlens=cache_seqlens,
conv_state_indices=conv_state_indices)
torch.testing.assert_close(out, out_gt, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(conv_state_clone, conv_state, rtol=1e-3, atol=1e-3)
@pytest.mark.skipif(not do_test(), reason='tilelang or causal_conv1d is not available')
class TestCausalConv1dFn:
@pytest.fixture
def device(self):
yield 'cuda'
@pytest.fixture
def hidden_size(self):
yield 2048
@pytest.fixture
def seqlen(self):
yield 4096
@pytest.fixture
def seq_idx(self, seqlen, device):
seq_idx = torch.zeros(seqlen, dtype=torch.int32, device=device)
seq_idx[seqlen // 4 * 3:] = 1
seq_idx = seq_idx.view(1, -1)
yield seq_idx
@pytest.fixture
def x(self, hidden_size, seqlen, device):
yield torch.randn(1, hidden_size, seqlen, device=device).transpose(1, 2).contiguous().transpose(1, 2)
@pytest.fixture
def weight(self, hidden_size, device):
yield torch.randn(hidden_size, 4, device=device)
@pytest.fixture
def bias(self, hidden_size, device):
yield torch.randn(hidden_size, device=device)
@pytest.fixture(params=[None, 'silu'])
def activation(self, request):
yield request.param
def test_causal_conv1d_fn(self, x, weight, bias, activation, seq_idx):
from causal_conv1d import causal_conv1d_fn as causal_conv1d_fn_gt
from lmdeploy.pytorch.kernels.cuda.causal_conv1d import causal_conv1d_fn
out = causal_conv1d_fn(x=x,
weight=weight,
bias=bias,
activation=activation,
return_final_states=False,
seq_idx=seq_idx)
out_gt = causal_conv1d_fn_gt(x=x,
weight=weight,
bias=bias,
activation=activation,
return_final_states=False,
seq_idx=seq_idx)
torch.testing.assert_close(out, out_gt, rtol=1e-3, atol=1e-3)
@pytest.mark.skipif(not do_test(), reason='tilelang or causal_conv1d is not available')
class TestCausalConv1dFnInitStates:
"""Test causal_conv1d_fn with per-sequence initial_states."""
@pytest.fixture
def device(self):
yield 'cuda'
@pytest.fixture
def hidden_size(self):
yield 2048
@pytest.fixture
def width(self):
yield 4
@pytest.fixture(params=[None, 'silu'])
def activation(self, request):
yield request.param
@pytest.fixture
def weight(self, hidden_size, width, device):
yield torch.randn(hidden_size, width, device=device)
@pytest.fixture
def bias(self, hidden_size, device):
yield torch.randn(hidden_size, device=device)
def _ref_conv1d(self, x_seq, weight, bias, init_state, activation):
"""Reference: prepend init_state and run F.conv1d per sequence."""
dim = weight.shape[0]
x_cat = torch.cat([init_state, x_seq], dim=-1).float()
w = weight.float().unsqueeze(1)
b = bias.float() if bias is not None else None
out = torch.nn.functional.conv1d(x_cat, w, b, padding=0, groups=dim)
out = out[..., :x_seq.shape[-1]]
if activation in ('silu', 'swish'):
out = torch.nn.functional.silu(out)
return out.to(x_seq.dtype)
def test_single_seq(self, hidden_size, width, weight, bias, activation, device):
"""Single sequence with non-zero initial states."""
seqlen = 128
x = torch.randn(1, hidden_size, seqlen, device=device).transpose(1, 2).contiguous().transpose(1, 2)
seq_idx = torch.zeros(1, seqlen, dtype=torch.int32, device=device)
init_states = torch.randn(1, hidden_size, width - 1, device=device, dtype=x.dtype)
from lmdeploy.pytorch.kernels.cuda.causal_conv1d import causal_conv1d_fn
out = causal_conv1d_fn(x=x, weight=weight, bias=bias, seq_idx=seq_idx, initial_states=init_states,
activation=activation)
out_ref = self._ref_conv1d(x, weight, bias, init_states, activation)
torch.testing.assert_close(out, out_ref, rtol=1e-3, atol=1e-3)
def test_multi_seq(self, hidden_size, width, weight, bias, activation, device):
"""Multiple sequences packed, each with its own initial state."""
seqlens = [100, 200, 50]
n_seqs = len(seqlens)
x_parts = [torch.randn(1, hidden_size, sl, device=device) for sl in seqlens]
# Make channel-last stride like real lmdeploy usage.
x_packed = torch.cat(x_parts, dim=-1).transpose(1, 2).contiguous().transpose(1, 2)
seq_idx_parts = [torch.full((sl, ), i, dtype=torch.int32, device=device) for i, sl in enumerate(seqlens)]
seq_idx = torch.cat(seq_idx_parts).unsqueeze(0)
init_states = torch.randn(n_seqs, hidden_size, width - 1, device=device, dtype=x_packed.dtype)
from lmdeploy.pytorch.kernels.cuda.causal_conv1d import causal_conv1d_fn
out = causal_conv1d_fn(x=x_packed, weight=weight, bias=bias, seq_idx=seq_idx, initial_states=init_states,
activation=activation)
# Build reference per-sequence.
offset = 0
for i, sl in enumerate(seqlens):
x_seq = x_packed[:, :, offset:offset + sl]
ref = self._ref_conv1d(x_seq, weight, bias, init_states[i:i + 1], activation)
torch.testing.assert_close(out[:, :, offset:offset + sl], ref, rtol=1e-3, atol=1e-3)
offset += sl
def test_zero_init_matches_no_init(self, hidden_size, width, weight, bias, activation, device):
"""Zero initial_states should produce the same result as no
initial_states."""
seqlens = [80, 120]
n_seqs = len(seqlens)
x_packed = torch.randn(1, hidden_size, sum(seqlens), device=device).transpose(1, 2).contiguous().transpose(
1, 2)
seq_idx_parts = [torch.full((sl, ), i, dtype=torch.int32, device=device) for i, sl in enumerate(seqlens)]
seq_idx = torch.cat(seq_idx_parts).unsqueeze(0)
zero_states = torch.zeros(n_seqs, hidden_size, width - 1, device=device, dtype=x_packed.dtype)
from lmdeploy.pytorch.kernels.cuda.causal_conv1d import causal_conv1d_fn
out_with_init = causal_conv1d_fn(x=x_packed, weight=weight, bias=bias, seq_idx=seq_idx,
initial_states=zero_states, activation=activation)
out_no_init = causal_conv1d_fn(x=x_packed, weight=weight, bias=bias, seq_idx=seq_idx, initial_states=None,
activation=activation)
torch.testing.assert_close(out_with_init, out_no_init, rtol=1e-3, atol=1e-3)
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