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[BugFix][Kernel]: fix illegal memory access in causal_conv1d when conv_states is None (#10928)

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Signed-off-by: xffxff <1247714429@qq.com>
This commit is contained in:
zhou fan
2024-12-08 01:21:18 +08:00
committed by GitHub
parent c889d5888b
commit 78029b34ed
2 changed files with 23 additions and 18 deletions
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39
tests/kernels/test_causal_conv1d.py
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@@ -149,13 +149,14 @@ def causal_conv1d_opcheck_fn(x: torch.Tensor,
@pytest.mark.parametrize("itype", [torch.bfloat16, torch.float])
@pytest.mark.parametrize("silu_activation", [True])
@pytest.mark.parametrize("has_bias", [True])
@pytest.mark.parametrize("has_initial_state", [True, False])
@pytest.mark.parametrize("width", [4])
@pytest.mark.parametrize(
'seqlen', [1, 8, 16, 32, 64, 128, 256, 512, 784, 1024, 1025, 2048, 4096])
@pytest.mark.parametrize('dim', [64])
@pytest.mark.parametrize('batch', [1])
def test_causal_conv1d(batch, dim, seqlen, width, has_bias, silu_activation,
itype):
has_initial_state, itype):
device = "cuda"
rtol, atol = (3e-4, 1e-3) if itype == torch.float32 else (3e-3, 5e-3)
if itype == torch.bfloat16:
@@ -167,11 +168,18 @@ def test_causal_conv1d(batch, dim, seqlen, width, has_bias, silu_activation,
weight = torch.randn(dim, width, device=device, dtype=itype)
bias = torch.randn(dim, device=device, dtype=itype) if has_bias else None
initial_states = torch.randn(batch,
dim,
width - 1,
device=device,
dtype=itype)
if has_initial_state:
initial_states = torch.randn(batch,
dim,
width - 1,
device=device,
dtype=itype)
has_initial_state_tensor = torch.ones(batch,
dtype=torch.bool,
device=x.device)
else:
initial_states = None
has_initial_state_tensor = None
x_ref = x.clone()
weight_ref = weight.clone()
bias_ref = bias.clone() if bias is not None else None
@@ -183,9 +191,7 @@ def test_causal_conv1d(batch, dim, seqlen, width, has_bias, silu_activation,
bias,
activation=activation,
conv_states=initial_states,
has_initial_state=torch.ones(batch,
dtype=torch.bool,
device=x.device))
has_initial_state=has_initial_state_tensor)
out_ref, final_states_ref = causal_conv1d_ref(
x_ref,
weight_ref,
@@ -193,11 +199,12 @@ def test_causal_conv1d(batch, dim, seqlen, width, has_bias, silu_activation,
initial_states=initial_states_ref,
return_final_states=True,
activation=activation)
assert initial_states is not None and final_states_ref is not None
assert torch.allclose(initial_states,
final_states_ref,
rtol=rtol,
atol=atol)
if has_initial_state:
assert initial_states is not None and final_states_ref is not None
assert torch.allclose(initial_states,
final_states_ref,
rtol=rtol,
atol=atol)
assert torch.allclose(out, out_ref, rtol=rtol, atol=atol)
causal_conv1d_opcheck_fn(x,
@@ -205,9 +212,7 @@ def test_causal_conv1d(batch, dim, seqlen, width, has_bias, silu_activation,
bias,
activation=activation,
conv_states=initial_states,
has_initial_state=torch.ones(batch,
dtype=torch.bool,
device=x.device))
has_initial_state=has_initial_state_tensor)
@pytest.mark.parametrize("itype", [torch.bfloat16])