[ExecuTorch][WebGPU] Op-tests for linear_qcs4w

[JCNTH]

Stack from ghstack (oldest at bottom):

• [ExecuTorch][WebGPU] Broadcast add.Tensor (mirror mul) + un-skip 6 models #21233
• [ExecuTorch][WebGPU] Add dynamic-shape resize hook to cat (output mismatch) #21232
• [ExecuTorch][WebGPU] Raise TensorMeta rank cap 4->8 to fix rank>4 ops (0x30) #21231
• [ExecuTorch][WebGPU] Add bitwise_or + logical_or ops (bool) #21230
• [ExecuTorch][WebGPU] Add linear_dq8ca_q4gsw + choose_qparams_affine (8da4w) #21229
• [ExecuTorch][WebGPU] Enable + golden int8 quantized ops (q8ta family + qcs4w/q8ta_q8csw linear) #21228
• [ExecuTorch][WebGPU] Enable + golden granular ops (elementwise/bool, shape/movement, pooling, conv/sampling) #21227
• [ExecuTorch][WebGPU] Add creation/cast ops (full family, scalar_tensor, _to_copy) + enable unary activation family #21226
• [ExecuTorch][WebGPU] Optimize reduction-family kernels to cooperative shared-memory #21225
• [ExecuTorch][WebGPU] Op-tests for bitwise_and + bitwise_not #21224
• [ExecuTorch][WebGPU] Add bitwise_and + bitwise_not ops (bool) #21223
• [ExecuTorch][WebGPU] Op-tests for logical_and #21222
• [ExecuTorch][WebGPU] Add logical_and op (aten.logical_and.default) #21221
• [ExecuTorch][WebGPU] Op-tests for comparisons + bool-output golden harness #21220
• [ExecuTorch][WebGPU] Add comparison ops (aten.eq/lt/le/gt/ge.Tensor -> bool) #21219
• [ExecuTorch][WebGPU] Op-tests for conv_with_clamp #21218
• [ExecuTorch][WebGPU] Add conv_with_clamp op (et_vk.conv_with_clamp) #21217
• [ExecuTorch][WebGPU] Op-tests for grid_priors #21216
• [ExecuTorch][WebGPU] Add grid_priors op (et_vk.grid_priors) #21215
• [ExecuTorch][WebGPU] Op-tests for linear_q8ta_q8csw #21214
• [ExecuTorch][WebGPU] Add linear_q8ta_q8csw op (et_vk.linear_q8ta_q8csw) #21213
• -> [ExecuTorch][WebGPU] Op-tests for linear_qcs4w #21212
• [ExecuTorch][WebGPU] Add linear_qcs4w op (et_vk.linear_qcs4w) #21211
• [ExecuTorch][WebGPU] Op-tests for argmax/argmin + int64-golden harness #21210
• [ExecuTorch][WebGPU] Add argmax/argmin ops + int64-output path #21209
• [ExecuTorch][WebGPU] Op-tests for floor_divide #21208
• [ExecuTorch][WebGPU] Add floor_divide op (aten.div.Tensor_mode) #21207
• [ExecuTorch][WebGPU] Op-tests for q8ta_conv2d_transposed #21206
• [ExecuTorch][WebGPU] Add q8ta_conv2d_transposed op (int8 transposed conv) #21205
• [ExecuTorch][WebGPU] Op-tests for q8ta_conv2d #21204
• [ExecuTorch][WebGPU] Add q8ta_conv2d op (int8 general conv) #21203
• [ExecuTorch][WebGPU] Op-tests for q8ta_conv2d_dw #21202
• [ExecuTorch][WebGPU] Add q8ta_conv2d_dw op (int8 depthwise conv) #21201
• [ExecuTorch][WebGPU] Op-tests for q8ta_conv2d_pw #21200
• [ExecuTorch][WebGPU] Add q8ta_conv2d_pw op (int8 pointwise conv) #21199
• [ExecuTorch][WebGPU] Op-tests for q8ta_linear #21198
• [ExecuTorch][WebGPU] Add q8ta_linear op (int8 quantized linear) #21197
• [ExecuTorch][WebGPU] Op-tests for q8ta_pixel_shuffle #21196
• [ExecuTorch][WebGPU] Add q8ta_pixel_shuffle op (int8 pixel_shuffle) #21195
• [ExecuTorch][WebGPU] Op-tests for q8ta_relu #21194
• [ExecuTorch][WebGPU] Add q8ta_relu op (int8 relu) #21193
• [ExecuTorch][WebGPU] Op-tests for q8ta_add #21192
• [ExecuTorch][WebGPU] Add q8ta_add op (int8 elementwise add) #21191
• [ExecuTorch][WebGPU] Op-tests for quantize/dequantize_per_tensor #21190
• [ExecuTorch][WebGPU] Op-test harness: int8-output golden support #21189
• [ExecuTorch][WebGPU] Add dequantize_per_tensor op (int8 buffer path) #21188
• [ExecuTorch][WebGPU] Add quantize_per_tensor op (int8 buffer path) #21187
• [ExecuTorch][WebGPU] Test apply_rotary_emb_interleaved #21186
• [ExecuTorch][WebGPU] Add apply_rotary_emb_interleaved op #21185
• [ExecuTorch][WebGPU] Op-tests for conv1d pointwise #21184
• [ExecuTorch][WebGPU] Port conv1d pointwise (aten.convolution) #21183
• [ExecuTorch][WebGPU] Op-tests for conv1d_dw (depthwise conv1d) #21182
• [ExecuTorch][WebGPU] Port aten.convolution (depthwise conv1d) #21181
• [ExecuTorch][WebGPU] Op-tests for grid_sampler_2d #21180
• [ExecuTorch][WebGPU] Port grid_sampler_2d #21179
• [ExecuTorch][WebGPU] Op-tests for pixel_shuffle #21178
• [ExecuTorch][WebGPU] Port pixel_shuffle #21177
• [ExecuTorch][WebGPU] Op-tests for avg_pool2d #21176
• [ExecuTorch][WebGPU] Port avg_pool2d #21175
• [ExecuTorch][WebGPU] Op-tests for native_group_norm #21174
• [ExecuTorch][WebGPU] Port native_group_norm #21173
• [ExecuTorch][WebGPU] Op-tests for index_select #21172
• [ExecuTorch][WebGPU] Port index_select #21171
• [ExecuTorch][WebGPU] Op-test framework: multi-output goldens #21170
• [ExecuTorch][WebGPU] Op-tests for repeat #21169
• [ExecuTorch][WebGPU] Port repeat #21168
• [ExecuTorch][WebGPU] Op-tests for flip #21167
• [ExecuTorch][WebGPU] Port flip #21166
• [ExecuTorch][WebGPU] Op-tests for amax + amin #21165
• [ExecuTorch][WebGPU] Port amax + amin (last-dim reductions) #21164
• [ExecuTorch][WebGPU] Op-test for pow.Tensor_Scalar #21163
• [ExecuTorch][WebGPU] Port pow.Tensor_Scalar #21162
• [ExecuTorch][WebGPU] Op-tests for pow #21161
• [ExecuTorch][WebGPU] Port pow (aten.pow.Tensor_Tensor) #21160
• [ExecuTorch][WebGPU] Op-test for minimum #21159
• [ExecuTorch][WebGPU] Port minimum (first A2 binary op) #21158
• [ExecuTorch][WebGPU] Op-tests for clamp + hardtanh #21157
• [ExecuTorch][WebGPU] Port clamp + hardtanh (clamp-family unary ops) #21156
• [ExecuTorch][WebGPU] Op-tests for the 10 unary activations #21155
• [ExecuTorch][WebGPU] Port 10 unary activations to the WebGPU backend #21154
• [ExecuTorch][WebGPU] Promote unary op plumbing to a shared 2D-dispatch helper #21153

Problem: The new et_vk.linear_qcs4w op needs golden coverage — and, being a quantized op reachable only through a specific quantizer, a test that actually produces the op and validates the fake-quant numerics.

Solution: make_qcs4w_linear_module runs a plain nn.Linear through the VulkanQuantizer weight-only 4-bit path (get_symmetric_quantization_config(is_dynamic=False, weight_bits=4) -> prepare_pt2e -> calibrate -> convert_pt2e), and the op-test module_factory returns the CONVERTED module, so the WebGPU output is goldened against the converted eager (fp32 per-channel fake-quant reference). This exercises the int4 unpack + per-channel dequant end-to-end. bias=False keeps the golden focused on qcs4w (a Linear bias lowers to a separate aten.add).

Implementation: cases.py registers linear_qcs4w with basic (4x32x16), gemv (M=1 decode shape), k64 (2x64x8), n32 (3x32x32) — all with K even (2 nibbles/byte) and N*ceil(K/2) % 4 == 0 (u32-packed weight); test_linear_qcs4w.py holds the module + a delegation smoke test asserting et_vk.linear_qcs4w is absorbed into the VulkanBackend delegate.
@exported-using-ghexport

Differential Revision: D112257668

Differential Revision: D112257668

[pytorch-bot]

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⏳ 13 Pending, 2 Unrelated Failures

As of commit 866e358 with merge base 4a26c64 ():

FLAKY - The following jobs failed but were likely due to flakiness present on trunk:

• pull / unittest / macos / macos-job (gh) (detected as infra flaky with no log or failing log classifier)
• pull / unittest-buck / macos / macos-job (gh) (detected as infra flaky with no log or failing log classifier)

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[psiddh]

Approving full WebGPU stack

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