[Bugfix] Fix incorrect original shape in hashing

[DarkLight1337]

Purpose

FIX https://github.com/vllm-project/vllm/pull/23623/files#r2301482573

Test Plan

Test Result

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• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
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[gemini-code-assist]

Code Review

This pull request correctly fixes a bug where the original shape of a bfloat16 tensor was being incorrectly determined during the hashing process, which could lead to hash collisions. While this fix is necessary, I've identified a pre-existing critical issue in the same code block that handles bfloat16 tensors. The method used to convert the tensor to its byte representation is incorrect and will result in a runtime error. I have provided a review comment with a suggested fix for this critical bug.

[gemini-code-assist]

The method used to get the byte representation of a bfloat16 tensor is incorrect and will cause a TypeError at runtime. The torch.Tensor.view() method expects a shape as input, not a dtype. Additionally, a bfloat16 tensor (2 bytes per element) cannot be directly viewed as a uint8 tensor (1 byte per element) by reinterpreting memory this way.

A more robust approach is to first view the bfloat16 tensor as a type of the same size (e.g., uint16), then convert it to a NumPy array, and finally view that array as uint8 to get the raw bytes for hashing.

Suggested change

	tensor_obj = tensor_obj.contiguous()
	tensor_obj = tensor_obj.view(
	(tensor_obj.numel(), )).view(torch.uint8)
	return cls.item_to_bytes(
	"tensor", {
	"original_dtype": str(tensor_dtype),
	"original_shape": tuple(tensor_obj.shape),
	"data": tensor_obj.numpy()
	"original_shape": tuple(tensor_shape),
	"data": tensor_obj.numpy(),
	})
	tensor_obj = tensor_obj.contiguous()
	# To correctly get the byte representation of a bfloat16 tensor,
	# it should be viewed as a type of the same size (e.g., uint16),
	# then converted to a numpy array, and finally viewed as uint8.
	data_np = tensor_obj.view(torch.uint16).numpy().view(np.uint8)
	return cls.item_to_bytes(
	"tensor", {
	"original_dtype": str(tensor_dtype),
	"original_shape": tuple(tensor_shape),
	"data": data_np,
	})