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sdpa: support attn_mask.requires_grad, support expanded number of heads in attn_mask #1563

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Dec 17, 2024
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sdpa: support attn_mask.requires_grad, support expanded number of heads in attn_mask #1563

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11 changes: 9 additions & 2 deletions thunder/executors/sdpaex.py
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Original file line number Diff line number Diff line change
Expand Up @@ -301,10 +301,12 @@ def _scaled_dot_product_efficient_attention_backward_impl(
if attn_mask is None:
grad_input_mask.append(False)
else:
grad_input_mask.append(attn_mask.requires_grad)
# Cannot rely on the requires_grad in the meta function,
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requires_grad of intermediate TensorProxies is ignored in our automatic differentiation code because we haven't done the work of properly threading this property through all computations.
We should remove the ability to query .requires_grad from intermediate TensorProxies completely to avoid similar bugs in the future. This can be achieved by introducing a separate "InputTensorProxy" which has this attribute and removing it from the regular TensorProxy.

# so here the gradient of attn_mask is always calculated
grad_input_mask.append(True)

# Reference: https://github.com/pytorch/pytorch/blob/v2.0.1/aten/src/ATen/native/transformers/cuda/attention_backward.cu#L394-L415
return torch.ops.aten._scaled_dot_product_efficient_attention_backward(
grad_q, grad_k, grad_v, grad_attn_mask = torch.ops.aten._scaled_dot_product_efficient_attention_backward(
grad_out,
_sdpa_enforce_input_tensor_contiguity(query),
_sdpa_enforce_input_tensor_contiguity(key),
Expand All @@ -319,6 +321,11 @@ def _scaled_dot_product_efficient_attention_backward_impl(
is_causal,
scale=scale,
)
if not utils.same_shape(grad_attn_mask.shape, attn_mask.shape):
# Needs to sum over the number of heads dimension in grad_attn_mask
# if the number of heads in attention mask is expanded in _attention_mask_memory_efficient_helper.
grad_attn_mask = torch.sum(grad_attn_mask, dim=1, keepdim=True)
return grad_q, grad_k, grad_v, grad_attn_mask


sdpea_bwd = sdpa_ex.register_operator(
Expand Down
41 changes: 41 additions & 0 deletions thunder/tests/test_sdpaex_executor.py
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Expand Up @@ -163,3 +163,44 @@ def fn(*args, **kwargs):

if result is not None:
return result


@pytest.mark.parametrize("dtype", [torch.float32, torch.float16, torch.bfloat16], ids=("f32", "f16", "bf16"))
@pytest.mark.parametrize("device,", ["cuda"])
@pytest.mark.parametrize("attn_mask_requires_grad", [True, False])
@requiresCUDA
def test_sdpa_attn_mask(attn_mask_requires_grad, device: str, dtype: torch.dtype):
# Enable math and memory-efficient sdpa options for Volta and prior devices
torch_device = torch.device(device)
if not device_version_support(torch_device, CudaVersion(8, 0), CudaVersion(9, 0)):
torch.backends.cuda.enable_mem_efficient_sdp(True)
torch.backends.cuda.enable_math_sdp(True)

def func(q, k, v, atten_mask):
tmp = atten_mask * atten_mask
return torch.nn.functional.scaled_dot_product_attention(q, k, v, tmp)

query = torch.randn(1, 28, 128, 128, dtype=dtype, device=device, requires_grad=True)
key = torch.randn(1, 28, 128, 128, dtype=dtype, device=device, requires_grad=True)
value = torch.randn(1, 28, 128, 128, dtype=dtype, device=device, requires_grad=True)
attn_mask = torch.randn(1, 1, 128, 128, dtype=dtype, device=device, requires_grad=attn_mask_requires_grad)

query1 = query.detach().clone().requires_grad_()
key1 = key.detach().clone().requires_grad_()
value1 = value.detach().clone().requires_grad_()
attn_mask1 = attn_mask.detach().clone().requires_grad_(attn_mask_requires_grad)

expected = func(query, key, value, attn_mask)
output = expected.mean()
output.backward()

jfun = thunder.jit(func)
actual = jfun(query1, key1, value1, attn_mask1)
output = actual.mean()
output.backward()

torch.testing.assert_close(actual, expected)
torch.testing.assert_close(attn_mask1.grad, attn_mask.grad)
torch.testing.assert_close(query.grad, query1.grad)
torch.testing.assert_close(key.grad, key1.grad)
torch.testing.assert_close(value.grad, value1.grad)
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