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Support llava clip vision model.
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@@ -211,6 +211,15 @@ class CLIPVision(torch.nn.Module):
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pooled_output = self.post_layernorm(x[:, 0, :])
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return x, i, pooled_output
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class LlavaProjector(torch.nn.Module):
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def __init__(self, in_dim, out_dim, dtype, device, operations):
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super().__init__()
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self.linear_1 = operations.Linear(in_dim, out_dim, bias=True, device=device, dtype=dtype)
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self.linear_2 = operations.Linear(out_dim, out_dim, bias=True, device=device, dtype=dtype)
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def forward(self, x):
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return self.linear_2(torch.nn.functional.gelu(self.linear_1(x[:, 1:])))
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class CLIPVisionModelProjection(torch.nn.Module):
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def __init__(self, config_dict, dtype, device, operations):
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super().__init__()
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@@ -220,7 +229,16 @@ class CLIPVisionModelProjection(torch.nn.Module):
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else:
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self.visual_projection = lambda a: a
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if "llava3" == config_dict.get("projector_type", None):
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self.multi_modal_projector = LlavaProjector(config_dict["hidden_size"], 4096, dtype, device, operations)
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else:
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self.multi_modal_projector = None
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def forward(self, *args, **kwargs):
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x = self.vision_model(*args, **kwargs)
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out = self.visual_projection(x[2])
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return (x[0], x[1], out)
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projected = None
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if self.multi_modal_projector is not None:
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projected = self.multi_modal_projector(x[1])
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return (x[0], x[1], out, projected)
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