Remove a redundant .softmax() in FlashDeformAttn.
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@@ -111,7 +111,6 @@ class FlashDeformAttn(nn.Module):
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value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads)
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sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2)
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attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points)
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attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points)
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# N, Len_q, n_heads, n_levels, n_points, 2
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if reference_points.shape[-1] == 2:
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offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1)
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@@ -131,7 +130,6 @@ class FlashDeformAttn(nn.Module):
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# Cat sampling_offsets and attention_weights, generate sampling_loc_attn:
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sampling_locations = sampling_locations.flatten(-3).half()
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attention_weights = attention_weights.flatten(-2)
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sampling_loc_attn = torch.cat([sampling_locations, attention_weights], dim=-1)
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output = FlashDeformAttnFunction.apply(
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