[ExecuTorch][WebGPU] Dynamic-shape integration test (allocate-at-max + per-op resize)

Pull Request resolved: #20582

**End-to-end validation that one graph built at the upper-bound seq-len serves every smaller live shape, matching the torch golden.**

**Problem:** the dynamic-resize engine (allocate-at-max buffers + per-op resize hooks + output resize) had unit-level reasoning but no single oracle proving a graph built at S=MAX runs correctly at S<MAX without reallocating buffers (which would invalidate bind groups).

**Solution:** a native test that builds each toy model at S=MAX and runs it at several live S, asserting the output matches a torch-computed golden and that the output EValue is resized to the live shape.

- Cases A-D: dynamic + static `rms_norm` (resize shrinks the dispatch; one reused graph across S proves buffers never move; static path unchanged).
- Cases F-H: `rms(rms(x))` cascade, `rms(x)+x` (rms->add cascade), `rms(x)*x` (mul).
- Cases I-L: dynamic `linear_q4gsw` (GEMM at several M), `sdpa_with_kv_cache` (GQA prefill at several S), `embedding_q4gsw` (int64 ids), `apply_rotary_emb` (two outputs).
- Cases M-N: dynamic `sigmoid` (elementwise) and `select_copy(0, -1)` (negative index resolved against the live leading dim each call).
- Graph-reuse variants: every dynamic op above (`rms_norm` incl. a grow-first smallest→largest order, the `rms(rms(x))` cascade, `linear_q4gsw`, `embedding_q4gsw`, `apply_rotary_emb`, `sigmoid`, `select_copy`) also runs ONE loaded graph across multiple live shapes — proving buffers never move so bind groups stay valid across every resize.

**Implementation:**
- `test/ops/dynamic_shape/test_dynamic_shape_export.py` exports each toy model through `VulkanPartitioner` with a dynamic dim and writes per-S torch goldens; reuses the existing op-test helpers for quant/sdpa/embedding/rope.
- `test/native/test_dynamic_shape.cpp` loads each `.pte`, runs each live S, and compares at the per-op tolerance (rms 1e-3, quant 5e-3, sdpa 2e-3). Reuse tests split each per-op helper into load-once + run-at-shape so a single `Module` serves the whole shape sweep.
- Multi-output ops select their output by full shape, never numel.

**Constraints:** numerics computed with torch (no hand-rolled reference); toy models stay within the 65535 1D-dispatch cap; SDPA case is skipped gracefully if `sym_size.int`/`copy_` op coverage is incomplete (does not fail the suite).

Co-authored-with: Claude Code.
ghstack-source-id: 399812841
@exported-using-ghexport

Differential Revision: [D109906090](https://our.internmc.facebook.com/intern/diff/D109906090/)

Author: JCNTH

backends/webgpu/CMakeLists.txt

@@ -194,6 +194,13 @@ if(EXECUTORCH_BUILD_WEBGPU_TEST)
     )
     target_compile_options(webgpu_op_test_util_test PRIVATE -fexceptions)
     set_property(TARGET webgpu_op_test_util_test PROPERTY CXX_STANDARD 17)
+
+    # Dynamic-shape integration test: a gtest binary with its own main() that
+    # brings up the device once (like webgpu_op_test).
+    add_webgpu_native_test(
+      webgpu_dynamic_shape_test test/native/test_dynamic_shape.cpp
+    )
+    target_link_libraries(webgpu_dynamic_shape_test PRIVATE GTest::gtest)
   endif()
   add_webgpu_native_test(webgpu_index_test test/native/test_index.cpp)
 endif()