What is the difference between.view(),.reshape(), and.contiguous() in PyTorch, and why does it matter?

These three methods deal with how a tensor's underlying memory is interpreted as a different shape. Understanding the difference prevents a class of confusing runtime errors related to tensor memory layout.

import torch

x = torch.arange(12).reshape(3, 4)    # shape (3, 4), contiguous memory

# ── .view(): ALWAYS returns a view (no copy), but requires contiguous memory
y = x.view(4, 3)     # works — x is contiguous
print(y.shape)        # (4, 3)

# ── Transpose breaks contiguity — the data is NOT rearranged in memory,
# only the strides describing how to read it change
xt = x.t()             # transpose — x.t() is a VIEW with different strides
print(xt.is_contiguous())  # False!

# This FAILS — view() cannot reinterpret non-contiguous memory
try:
    xt.view(3, 4)
except RuntimeError as e:
    print(f"Error: {e}")
# RuntimeError: view size is not compatible with input tensor's size and stride

# ── .reshape(): tries view() first; falls back to copying if needed
z = xt.reshape(3, 4)   # WORKS — automatically copies if necessary
print(z.shape)          # (3, 4)

# ── .contiguous(): explicitly forces a contiguous copy in memory
xt_contig = xt.contiguous()
print(xt_contig.is_contiguous())  # True
xt_contig.view(3, 4)   # now works, since it is contiguous

# Strides explain WHY this happens
print(x.stride())   # (4, 1) — contiguous: move 1 step = 1 memory address
print(xt.stride())  # (1, 4) — transposed: strides reflect the swap, no copy made

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