[executorch][webgpu] Test linear embedding dynamic reuse#21469

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Merged
JCNTH merged 1 commit into from
Jul 29, 2026
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[executorch][webgpu] Test linear embedding dynamic reuse #21469

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25 changes: 17 additions & 8 deletions backends/webgpu/test/native/test_dynamic_shape.cpp
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -193,31 +193,31 @@ void check_sdpa(int s) {
constexpr int kEmbDim = 64;
// Run emb_dyn at N tokens on an already-loaded module (so it can be reused
// across N), and compare to the golden.
void run_embedding(Module& m, int n) {
const std::string b = g_dir + "/emb_dyn.S" + std::to_string(n) + ".";
void run_embedding(Module& m, int n, const char* prefix = "emb_dyn") {
const std::string b = g_dir + "/" + prefix + ".S" + std::to_string(n) + ".";
std::ifstream f(b + "idx.bin", std::ios::binary | std::ios::ate);
ASSERT_TRUE(f.good()) << "missing emb_dyn.S" << n;
ASSERT_TRUE(f.good()) << "missing " << prefix << ".S" << n;
const std::streamsize nb = f.tellg();
ASSERT_GE(nb, 0) << "missing emb_dyn.S" << n;
ASSERT_GE(nb, 0) << "missing " << prefix << ".S" << n;
f.seekg(0);
std::vector<int64_t> idx(static_cast<size_t>(nb) / sizeof(int64_t));
f.read(reinterpret_cast<char*>(idx.data()), nb);
ASSERT_EQ(idx.size(), static_cast<size_t>(n))
<< "wrong emb_dyn idx size S" << n;
<< "wrong " << prefix << " idx size S" << n;
auto golden = read_bin(b + "golden.bin");
auto t = make_tensor_ptr({n}, std::move(idx)); // int64 (Long) host input
auto r = m.forward({EValue(t)});
ASSERT_TRUE(r.ok() && !r.get().empty() && r.get()[0].isTensor())
<< "emb N=" << n
<< prefix << " N=" << n
<< " forward failed (err=" << (r.ok() ? 0 : (int)r.error()) << ")";
const auto& out = r.get()[0].toTensor();
const size_t numel = static_cast<size_t>(n) * kEmbDim;
ASSERT_EQ(static_cast<size_t>(out.numel()), numel)
<< "emb N=" << n << " output numel mismatch";
<< prefix << " N=" << n << " output numel mismatch";
std::vector<float> got(
out.const_data_ptr<float>(), out.const_data_ptr<float>() + numel);
const float e = max_err(got, golden);
EXPECT_LT(e, 5e-3f) << "emb_dyn N=" << n << " max_err=" << e;
EXPECT_LT(e, 5e-3f) << prefix << " N=" << n << " max_err=" << e;
}

void check_embedding(int n) {
Expand DownExpand Up@@ -459,6 +459,15 @@ TEST(DynamicShape, EmbeddingReusedGraph) {
}
}

// K3: linear-packed reuse must preserve nibble order across resizes.
TEST(DynamicShape, LinearPackedEmbeddingReusedGraph) {
Module m(g_dir + "/emb_dyn_linear.pte");
ASSERT_EQ(m.load_forward(), Error::Ok) << "load emb_dyn_linear.pte";
for (int n : {16, 8, 1, 16}) {
run_embedding(m, n, "emb_dyn_linear");
}
}

// L: dynamic RoPE (two outputs) at several seq-len S.
TEST(DynamicShape, Rope) {
for (int s : {16, 8, 1}) {
Expand Down
76 changes: 63 additions & 13 deletions backends/webgpu/test/ops/dynamic_shape/test_dynamic_shape_export.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -322,8 +322,53 @@ def cfg(s: int) -> "SdpaConfig":
EMB_MAXN = 16


class _LinearPackedEmbedding(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
packed = torch.arange(EMB_VOCAB * (EMB_DIM // 2), dtype=torch.int64).reshape(
EMB_VOCAB, EMB_DIM // 2
)
self.register_buffer("weight", (packed % 256).to(torch.uint8))
self.register_buffer("scales", torch.ones(EMB_VOCAB, EMB_DIM // EMB_GROUP))

def forward(self, indices: torch.Tensor) -> torch.Tensor:
return torch.ops.et_vk.embedding_q4gsw.default(
self.weight, self.scales, EMB_GROUP, indices, True
)


def _write_embedding_goldens(
out_dir: str,
prefix: str,
weight: torch.Tensor,
scales: torch.Tensor,
group_size: int,
is_linear: bool,
) -> None:
for n in [EMB_MAXN, 8, 1]:
idx = (torch.arange(n, dtype=torch.long) * 7) % EMB_VOCAB
golden = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, is_linear
)
if is_linear:
nonlinear_golden = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, False
)
if torch.equal(golden, nonlinear_golden):
raise RuntimeError(
"emb_dyn_linear fixture does not distinguish nibble packing"
)
idx.detach().numpy().astype("<i8").tofile(
os.path.join(out_dir, f"{prefix}.S{n}.idx.bin")
)
golden.detach().numpy().astype("<f4").tofile(
os.path.join(out_dir, f"{prefix}.S{n}.golden.bin")
)
print(f" golden {prefix} N={n} (shape {tuple(golden.shape)})")


def _export_dynamic_embedding(out_dir: str) -> None:
from executorch.backends.webgpu.test.ops.embedding_q4gsw.test_embedding_q4gsw import (
from executorch.backends.webgpu.test.ops.test_embedding_q4gsw import (
_make_quantized_model,
_quant_params,
Shape,
Expand All@@ -346,18 +391,23 @@ def _export_dynamic_embedding(out_dir: str) -> None:
f.write(et.buffer)
print("Exported emb_dyn.pte")
weight, scales, group_size = _quant_params(qm)
for n in [EMB_MAXN, 8, 1]:
idx = (torch.arange(n, dtype=torch.long) * 7) % EMB_VOCAB
g = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, False
)
idx.detach().numpy().astype("<i8").tofile(
os.path.join(out_dir, f"emb_dyn.S{n}.idx.bin")
)
g.detach().numpy().astype("<f4").tofile(
os.path.join(out_dir, f"emb_dyn.S{n}.golden.bin")
)
print(f" golden emb_dyn N={n} (shape {tuple(g.shape)})")
_write_embedding_goldens(out_dir, "emb_dyn", weight, scales, group_size, False)

linear_model = _LinearPackedEmbedding().eval()
_export(
linear_model,
(idx_max,),
({0: n_dim},),
os.path.join(out_dir, "emb_dyn_linear.pte"),
)
_write_embedding_goldens(
out_dir,
"emb_dyn_linear",
linear_model.weight,
linear_model.scales,
EMB_GROUP,
True,
)


# Dynamic RoPE: xq/xk + freqs all share a dynamic seq-len S.
Expand Down
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all \u003cpre\u003e\u003ccode\u003e blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks"); } } catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); } })(); (function(){ try { var __m = "github.com"; var __re = new RegExp('^' + "github\\.com" + '
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[executorch][webgpu] Test linear embedding dynamic reuse#21469

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Merged
JCNTH merged 1 commit into from
Jul 29, 2026
Merged

[executorch][webgpu] Test linear embedding dynamic reuse #21469

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25 changes: 17 additions & 8 deletions backends/webgpu/test/native/test_dynamic_shape.cpp
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -193,31 +193,31 @@ void check_sdpa(int s) {
constexpr int kEmbDim = 64;
// Run emb_dyn at N tokens on an already-loaded module (so it can be reused
// across N), and compare to the golden.
void run_embedding(Module& m, int n) {
const std::string b = g_dir + "/emb_dyn.S" + std::to_string(n) + ".";
void run_embedding(Module& m, int n, const char* prefix = "emb_dyn") {
const std::string b = g_dir + "/" + prefix + ".S" + std::to_string(n) + ".";
std::ifstream f(b + "idx.bin", std::ios::binary | std::ios::ate);
ASSERT_TRUE(f.good()) << "missing emb_dyn.S" << n;
ASSERT_TRUE(f.good()) << "missing " << prefix << ".S" << n;
const std::streamsize nb = f.tellg();
ASSERT_GE(nb, 0) << "missing emb_dyn.S" << n;
ASSERT_GE(nb, 0) << "missing " << prefix << ".S" << n;
f.seekg(0);
std::vector<int64_t> idx(static_cast<size_t>(nb) / sizeof(int64_t));
f.read(reinterpret_cast<char*>(idx.data()), nb);
ASSERT_EQ(idx.size(), static_cast<size_t>(n))
<< "wrong emb_dyn idx size S" << n;
<< "wrong " << prefix << " idx size S" << n;
auto golden = read_bin(b + "golden.bin");
auto t = make_tensor_ptr({n}, std::move(idx)); // int64 (Long) host input
auto r = m.forward({EValue(t)});
ASSERT_TRUE(r.ok() && !r.get().empty() && r.get()[0].isTensor())
<< "emb N=" << n
<< prefix << " N=" << n
<< " forward failed (err=" << (r.ok() ? 0 : (int)r.error()) << ")";
const auto& out = r.get()[0].toTensor();
const size_t numel = static_cast<size_t>(n) * kEmbDim;
ASSERT_EQ(static_cast<size_t>(out.numel()), numel)
<< "emb N=" << n << " output numel mismatch";
<< prefix << " N=" << n << " output numel mismatch";
std::vector<float> got(
out.const_data_ptr<float>(), out.const_data_ptr<float>() + numel);
const float e = max_err(got, golden);
EXPECT_LT(e, 5e-3f) << "emb_dyn N=" << n << " max_err=" << e;
EXPECT_LT(e, 5e-3f) << prefix << " N=" << n << " max_err=" << e;
}

void check_embedding(int n) {
Expand DownExpand Up@@ -459,6 +459,15 @@ TEST(DynamicShape, EmbeddingReusedGraph) {
}
}

// K3: linear-packed reuse must preserve nibble order across resizes.
TEST(DynamicShape, LinearPackedEmbeddingReusedGraph) {
Module m(g_dir + "/emb_dyn_linear.pte");
ASSERT_EQ(m.load_forward(), Error::Ok) << "load emb_dyn_linear.pte";
for (int n : {16, 8, 1, 16}) {
run_embedding(m, n, "emb_dyn_linear");
}
}

// L: dynamic RoPE (two outputs) at several seq-len S.
TEST(DynamicShape, Rope) {
for (int s : {16, 8, 1}) {
Expand Down
76 changes: 63 additions & 13 deletions backends/webgpu/test/ops/dynamic_shape/test_dynamic_shape_export.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -322,8 +322,53 @@ def cfg(s: int) -> "SdpaConfig":
EMB_MAXN = 16


class _LinearPackedEmbedding(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
packed = torch.arange(EMB_VOCAB * (EMB_DIM // 2), dtype=torch.int64).reshape(
EMB_VOCAB, EMB_DIM // 2
)
self.register_buffer("weight", (packed % 256).to(torch.uint8))
self.register_buffer("scales", torch.ones(EMB_VOCAB, EMB_DIM // EMB_GROUP))

def forward(self, indices: torch.Tensor) -> torch.Tensor:
return torch.ops.et_vk.embedding_q4gsw.default(
self.weight, self.scales, EMB_GROUP, indices, True
)


def _write_embedding_goldens(
out_dir: str,
prefix: str,
weight: torch.Tensor,
scales: torch.Tensor,
group_size: int,
is_linear: bool,
) -> None:
for n in [EMB_MAXN, 8, 1]:
idx = (torch.arange(n, dtype=torch.long) * 7) % EMB_VOCAB
golden = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, is_linear
)
if is_linear:
nonlinear_golden = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, False
)
if torch.equal(golden, nonlinear_golden):
raise RuntimeError(
"emb_dyn_linear fixture does not distinguish nibble packing"
)
idx.detach().numpy().astype("<i8").tofile(
os.path.join(out_dir, f"{prefix}.S{n}.idx.bin")
)
golden.detach().numpy().astype("<f4").tofile(
os.path.join(out_dir, f"{prefix}.S{n}.golden.bin")
)
print(f" golden {prefix} N={n} (shape {tuple(golden.shape)})")


def _export_dynamic_embedding(out_dir: str) -> None:
from executorch.backends.webgpu.test.ops.embedding_q4gsw.test_embedding_q4gsw import (
from executorch.backends.webgpu.test.ops.test_embedding_q4gsw import (
_make_quantized_model,
_quant_params,
Shape,
Expand All@@ -346,18 +391,23 @@ def _export_dynamic_embedding(out_dir: str) -> None:
f.write(et.buffer)
print("Exported emb_dyn.pte")
weight, scales, group_size = _quant_params(qm)
for n in [EMB_MAXN, 8, 1]:
idx = (torch.arange(n, dtype=torch.long) * 7) % EMB_VOCAB
g = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, False
)
idx.detach().numpy().astype("<i8").tofile(
os.path.join(out_dir, f"emb_dyn.S{n}.idx.bin")
)
g.detach().numpy().astype("<f4").tofile(
os.path.join(out_dir, f"emb_dyn.S{n}.golden.bin")
)
print(f" golden emb_dyn N={n} (shape {tuple(g.shape)})")
_write_embedding_goldens(out_dir, "emb_dyn", weight, scales, group_size, False)

linear_model = _LinearPackedEmbedding().eval()
_export(
linear_model,
(idx_max,),
({0: n_dim},),
os.path.join(out_dir, "emb_dyn_linear.pte"),
)
_write_embedding_goldens(
out_dir,
"emb_dyn_linear",
linear_model.weight,
linear_model.scales,
EMB_GROUP,
True,
)


# Dynamic RoPE: xq/xk + freqs all share a dynamic seq-len S.
Expand Down
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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[executorch][webgpu] Test linear embedding dynamic reuse#21469

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Merged
JCNTH merged 1 commit into from
Jul 29, 2026
Merged

[executorch][webgpu] Test linear embedding dynamic reuse #21469

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25 changes: 17 additions & 8 deletions backends/webgpu/test/native/test_dynamic_shape.cpp
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -193,31 +193,31 @@ void check_sdpa(int s) {
constexpr int kEmbDim = 64;
// Run emb_dyn at N tokens on an already-loaded module (so it can be reused
// across N), and compare to the golden.
void run_embedding(Module& m, int n) {
const std::string b = g_dir + "/emb_dyn.S" + std::to_string(n) + ".";
void run_embedding(Module& m, int n, const char* prefix = "emb_dyn") {
const std::string b = g_dir + "/" + prefix + ".S" + std::to_string(n) + ".";
std::ifstream f(b + "idx.bin", std::ios::binary | std::ios::ate);
ASSERT_TRUE(f.good()) << "missing emb_dyn.S" << n;
ASSERT_TRUE(f.good()) << "missing " << prefix << ".S" << n;
const std::streamsize nb = f.tellg();
ASSERT_GE(nb, 0) << "missing emb_dyn.S" << n;
ASSERT_GE(nb, 0) << "missing " << prefix << ".S" << n;
f.seekg(0);
std::vector<int64_t> idx(static_cast<size_t>(nb) / sizeof(int64_t));
f.read(reinterpret_cast<char*>(idx.data()), nb);
ASSERT_EQ(idx.size(), static_cast<size_t>(n))
<< "wrong emb_dyn idx size S" << n;
<< "wrong " << prefix << " idx size S" << n;
auto golden = read_bin(b + "golden.bin");
auto t = make_tensor_ptr({n}, std::move(idx)); // int64 (Long) host input
auto r = m.forward({EValue(t)});
ASSERT_TRUE(r.ok() && !r.get().empty() && r.get()[0].isTensor())
<< "emb N=" << n
<< prefix << " N=" << n
<< " forward failed (err=" << (r.ok() ? 0 : (int)r.error()) << ")";
const auto& out = r.get()[0].toTensor();
const size_t numel = static_cast<size_t>(n) * kEmbDim;
ASSERT_EQ(static_cast<size_t>(out.numel()), numel)
<< "emb N=" << n << " output numel mismatch";
<< prefix << " N=" << n << " output numel mismatch";
std::vector<float> got(
out.const_data_ptr<float>(), out.const_data_ptr<float>() + numel);
const float e = max_err(got, golden);
EXPECT_LT(e, 5e-3f) << "emb_dyn N=" << n << " max_err=" << e;
EXPECT_LT(e, 5e-3f) << prefix << " N=" << n << " max_err=" << e;
}

void check_embedding(int n) {
Expand DownExpand Up@@ -459,6 +459,15 @@ TEST(DynamicShape, EmbeddingReusedGraph) {
}
}

// K3: linear-packed reuse must preserve nibble order across resizes.
TEST(DynamicShape, LinearPackedEmbeddingReusedGraph) {
Module m(g_dir + "/emb_dyn_linear.pte");
ASSERT_EQ(m.load_forward(), Error::Ok) << "load emb_dyn_linear.pte";
for (int n : {16, 8, 1, 16}) {
run_embedding(m, n, "emb_dyn_linear");
}
}

// L: dynamic RoPE (two outputs) at several seq-len S.
TEST(DynamicShape, Rope) {
for (int s : {16, 8, 1}) {
Expand Down
76 changes: 63 additions & 13 deletions backends/webgpu/test/ops/dynamic_shape/test_dynamic_shape_export.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -322,8 +322,53 @@ def cfg(s: int) -> "SdpaConfig":
EMB_MAXN = 16


class _LinearPackedEmbedding(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
packed = torch.arange(EMB_VOCAB * (EMB_DIM // 2), dtype=torch.int64).reshape(
EMB_VOCAB, EMB_DIM // 2
)
self.register_buffer("weight", (packed % 256).to(torch.uint8))
self.register_buffer("scales", torch.ones(EMB_VOCAB, EMB_DIM // EMB_GROUP))

def forward(self, indices: torch.Tensor) -> torch.Tensor:
return torch.ops.et_vk.embedding_q4gsw.default(
self.weight, self.scales, EMB_GROUP, indices, True
)


def _write_embedding_goldens(
out_dir: str,
prefix: str,
weight: torch.Tensor,
scales: torch.Tensor,
group_size: int,
is_linear: bool,
) -> None:
for n in [EMB_MAXN, 8, 1]:
idx = (torch.arange(n, dtype=torch.long) * 7) % EMB_VOCAB
golden = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, is_linear
)
if is_linear:
nonlinear_golden = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, False
)
if torch.equal(golden, nonlinear_golden):
raise RuntimeError(
"emb_dyn_linear fixture does not distinguish nibble packing"
)
idx.detach().numpy().astype("<i8").tofile(
os.path.join(out_dir, f"{prefix}.S{n}.idx.bin")
)
golden.detach().numpy().astype("<f4").tofile(
os.path.join(out_dir, f"{prefix}.S{n}.golden.bin")
)
print(f" golden {prefix} N={n} (shape {tuple(golden.shape)})")


def _export_dynamic_embedding(out_dir: str) -> None:
from executorch.backends.webgpu.test.ops.embedding_q4gsw.test_embedding_q4gsw import (
from executorch.backends.webgpu.test.ops.test_embedding_q4gsw import (
_make_quantized_model,
_quant_params,
Shape,
Expand All@@ -346,18 +391,23 @@ def _export_dynamic_embedding(out_dir: str) -> None:
f.write(et.buffer)
print("Exported emb_dyn.pte")
weight, scales, group_size = _quant_params(qm)
for n in [EMB_MAXN, 8, 1]:
idx = (torch.arange(n, dtype=torch.long) * 7) % EMB_VOCAB
g = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, False
)
idx.detach().numpy().astype("<i8").tofile(
os.path.join(out_dir, f"emb_dyn.S{n}.idx.bin")
)
g.detach().numpy().astype("<f4").tofile(
os.path.join(out_dir, f"emb_dyn.S{n}.golden.bin")
)
print(f" golden emb_dyn N={n} (shape {tuple(g.shape)})")
_write_embedding_goldens(out_dir, "emb_dyn", weight, scales, group_size, False)

linear_model = _LinearPackedEmbedding().eval()
_export(
linear_model,
(idx_max,),
({0: n_dim},),
os.path.join(out_dir, "emb_dyn_linear.pte"),
)
_write_embedding_goldens(
out_dir,
"emb_dyn_linear",
linear_model.weight,
linear_model.scales,
EMB_GROUP,
True,
)


# Dynamic RoPE: xq/xk + freqs all share a dynamic seq-len S.
Expand Down
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length \u003e 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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[executorch][webgpu] Test linear embedding dynamic reuse#21469

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Merged
JCNTH merged 1 commit into from
Jul 29, 2026
Merged

[executorch][webgpu] Test linear embedding dynamic reuse #21469

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25 changes: 17 additions & 8 deletions backends/webgpu/test/native/test_dynamic_shape.cpp
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -193,31 +193,31 @@ void check_sdpa(int s) {
constexpr int kEmbDim = 64;
// Run emb_dyn at N tokens on an already-loaded module (so it can be reused
// across N), and compare to the golden.
void run_embedding(Module& m, int n) {
const std::string b = g_dir + "/emb_dyn.S" + std::to_string(n) + ".";
void run_embedding(Module& m, int n, const char* prefix = "emb_dyn") {
const std::string b = g_dir + "/" + prefix + ".S" + std::to_string(n) + ".";
std::ifstream f(b + "idx.bin", std::ios::binary | std::ios::ate);
ASSERT_TRUE(f.good()) << "missing emb_dyn.S" << n;
ASSERT_TRUE(f.good()) << "missing " << prefix << ".S" << n;
const std::streamsize nb = f.tellg();
ASSERT_GE(nb, 0) << "missing emb_dyn.S" << n;
ASSERT_GE(nb, 0) << "missing " << prefix << ".S" << n;
f.seekg(0);
std::vector<int64_t> idx(static_cast<size_t>(nb) / sizeof(int64_t));
f.read(reinterpret_cast<char*>(idx.data()), nb);
ASSERT_EQ(idx.size(), static_cast<size_t>(n))
<< "wrong emb_dyn idx size S" << n;
<< "wrong " << prefix << " idx size S" << n;
auto golden = read_bin(b + "golden.bin");
auto t = make_tensor_ptr({n}, std::move(idx)); // int64 (Long) host input
auto r = m.forward({EValue(t)});
ASSERT_TRUE(r.ok() && !r.get().empty() && r.get()[0].isTensor())
<< "emb N=" << n
<< prefix << " N=" << n
<< " forward failed (err=" << (r.ok() ? 0 : (int)r.error()) << ")";
const auto& out = r.get()[0].toTensor();
const size_t numel = static_cast<size_t>(n) * kEmbDim;
ASSERT_EQ(static_cast<size_t>(out.numel()), numel)
<< "emb N=" << n << " output numel mismatch";
<< prefix << " N=" << n << " output numel mismatch";
std::vector<float> got(
out.const_data_ptr<float>(), out.const_data_ptr<float>() + numel);
const float e = max_err(got, golden);
EXPECT_LT(e, 5e-3f) << "emb_dyn N=" << n << " max_err=" << e;
EXPECT_LT(e, 5e-3f) << prefix << " N=" << n << " max_err=" << e;
}

void check_embedding(int n) {
Expand DownExpand Up@@ -459,6 +459,15 @@ TEST(DynamicShape, EmbeddingReusedGraph) {
}
}

// K3: linear-packed reuse must preserve nibble order across resizes.
TEST(DynamicShape, LinearPackedEmbeddingReusedGraph) {
Module m(g_dir + "/emb_dyn_linear.pte");
ASSERT_EQ(m.load_forward(), Error::Ok) << "load emb_dyn_linear.pte";
for (int n : {16, 8, 1, 16}) {
run_embedding(m, n, "emb_dyn_linear");
}
}

// L: dynamic RoPE (two outputs) at several seq-len S.
TEST(DynamicShape, Rope) {
for (int s : {16, 8, 1}) {
Expand Down
76 changes: 63 additions & 13 deletions backends/webgpu/test/ops/dynamic_shape/test_dynamic_shape_export.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -322,8 +322,53 @@ def cfg(s: int) -> "SdpaConfig":
EMB_MAXN = 16


class _LinearPackedEmbedding(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
packed = torch.arange(EMB_VOCAB * (EMB_DIM // 2), dtype=torch.int64).reshape(
EMB_VOCAB, EMB_DIM // 2
)
self.register_buffer("weight", (packed % 256).to(torch.uint8))
self.register_buffer("scales", torch.ones(EMB_VOCAB, EMB_DIM // EMB_GROUP))

def forward(self, indices: torch.Tensor) -> torch.Tensor:
return torch.ops.et_vk.embedding_q4gsw.default(
self.weight, self.scales, EMB_GROUP, indices, True
)


def _write_embedding_goldens(
out_dir: str,
prefix: str,
weight: torch.Tensor,
scales: torch.Tensor,
group_size: int,
is_linear: bool,
) -> None:
for n in [EMB_MAXN, 8, 1]:
idx = (torch.arange(n, dtype=torch.long) * 7) % EMB_VOCAB
golden = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, is_linear
)
if is_linear:
nonlinear_golden = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, False
)
if torch.equal(golden, nonlinear_golden):
raise RuntimeError(
"emb_dyn_linear fixture does not distinguish nibble packing"
)
idx.detach().numpy().astype("<i8").tofile(
os.path.join(out_dir, f"{prefix}.S{n}.idx.bin")
)
golden.detach().numpy().astype("<f4").tofile(
os.path.join(out_dir, f"{prefix}.S{n}.golden.bin")
)
print(f" golden {prefix} N={n} (shape {tuple(golden.shape)})")


def _export_dynamic_embedding(out_dir: str) -> None:
from executorch.backends.webgpu.test.ops.embedding_q4gsw.test_embedding_q4gsw import (
from executorch.backends.webgpu.test.ops.test_embedding_q4gsw import (
_make_quantized_model,
_quant_params,
Shape,
Expand All@@ -346,18 +391,23 @@ def _export_dynamic_embedding(out_dir: str) -> None:
f.write(et.buffer)
print("Exported emb_dyn.pte")
weight, scales, group_size = _quant_params(qm)
for n in [EMB_MAXN, 8, 1]:
idx = (torch.arange(n, dtype=torch.long) * 7) % EMB_VOCAB
g = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, False
)
idx.detach().numpy().astype("<i8").tofile(
os.path.join(out_dir, f"emb_dyn.S{n}.idx.bin")
)
g.detach().numpy().astype("<f4").tofile(
os.path.join(out_dir, f"emb_dyn.S{n}.golden.bin")
)
print(f" golden emb_dyn N={n} (shape {tuple(g.shape)})")
_write_embedding_goldens(out_dir, "emb_dyn", weight, scales, group_size, False)

linear_model = _LinearPackedEmbedding().eval()
_export(
linear_model,
(idx_max,),
({0: n_dim},),
os.path.join(out_dir, "emb_dyn_linear.pte"),
)
_write_embedding_goldens(
out_dir,
"emb_dyn_linear",
linear_model.weight,
linear_model.scales,
EMB_GROUP,
True,
)


# Dynamic RoPE: xq/xk + freqs all share a dynamic seq-len S.
Expand Down
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
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[executorch][webgpu] Test linear embedding dynamic reuse#21469

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Merged
JCNTH merged 1 commit into from
Jul 29, 2026
Merged

[executorch][webgpu] Test linear embedding dynamic reuse #21469

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25 changes: 17 additions & 8 deletions backends/webgpu/test/native/test_dynamic_shape.cpp
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -193,31 +193,31 @@ void check_sdpa(int s) {
constexpr int kEmbDim = 64;
// Run emb_dyn at N tokens on an already-loaded module (so it can be reused
// across N), and compare to the golden.
void run_embedding(Module& m, int n) {
const std::string b = g_dir + "/emb_dyn.S" + std::to_string(n) + ".";
void run_embedding(Module& m, int n, const char* prefix = "emb_dyn") {
const std::string b = g_dir + "/" + prefix + ".S" + std::to_string(n) + ".";
std::ifstream f(b + "idx.bin", std::ios::binary | std::ios::ate);
ASSERT_TRUE(f.good()) << "missing emb_dyn.S" << n;
ASSERT_TRUE(f.good()) << "missing " << prefix << ".S" << n;
const std::streamsize nb = f.tellg();
ASSERT_GE(nb, 0) << "missing emb_dyn.S" << n;
ASSERT_GE(nb, 0) << "missing " << prefix << ".S" << n;
f.seekg(0);
std::vector<int64_t> idx(static_cast<size_t>(nb) / sizeof(int64_t));
f.read(reinterpret_cast<char*>(idx.data()), nb);
ASSERT_EQ(idx.size(), static_cast<size_t>(n))
<< "wrong emb_dyn idx size S" << n;
<< "wrong " << prefix << " idx size S" << n;
auto golden = read_bin(b + "golden.bin");
auto t = make_tensor_ptr({n}, std::move(idx)); // int64 (Long) host input
auto r = m.forward({EValue(t)});
ASSERT_TRUE(r.ok() && !r.get().empty() && r.get()[0].isTensor())
<< "emb N=" << n
<< prefix << " N=" << n
<< " forward failed (err=" << (r.ok() ? 0 : (int)r.error()) << ")";
const auto& out = r.get()[0].toTensor();
const size_t numel = static_cast<size_t>(n) * kEmbDim;
ASSERT_EQ(static_cast<size_t>(out.numel()), numel)
<< "emb N=" << n << " output numel mismatch";
<< prefix << " N=" << n << " output numel mismatch";
std::vector<float> got(
out.const_data_ptr<float>(), out.const_data_ptr<float>() + numel);
const float e = max_err(got, golden);
EXPECT_LT(e, 5e-3f) << "emb_dyn N=" << n << " max_err=" << e;
EXPECT_LT(e, 5e-3f) << prefix << " N=" << n << " max_err=" << e;
}

void check_embedding(int n) {
Expand DownExpand Up@@ -459,6 +459,15 @@ TEST(DynamicShape, EmbeddingReusedGraph) {
}
}

// K3: linear-packed reuse must preserve nibble order across resizes.
TEST(DynamicShape, LinearPackedEmbeddingReusedGraph) {
Module m(g_dir + "/emb_dyn_linear.pte");
ASSERT_EQ(m.load_forward(), Error::Ok) << "load emb_dyn_linear.pte";
for (int n : {16, 8, 1, 16}) {
run_embedding(m, n, "emb_dyn_linear");
}
}

// L: dynamic RoPE (two outputs) at several seq-len S.
TEST(DynamicShape, Rope) {
for (int s : {16, 8, 1}) {
Expand Down
76 changes: 63 additions & 13 deletions backends/webgpu/test/ops/dynamic_shape/test_dynamic_shape_export.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -322,8 +322,53 @@ def cfg(s: int) -> "SdpaConfig":
EMB_MAXN = 16


class _LinearPackedEmbedding(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
packed = torch.arange(EMB_VOCAB * (EMB_DIM // 2), dtype=torch.int64).reshape(
EMB_VOCAB, EMB_DIM // 2
)
self.register_buffer("weight", (packed % 256).to(torch.uint8))
self.register_buffer("scales", torch.ones(EMB_VOCAB, EMB_DIM // EMB_GROUP))

def forward(self, indices: torch.Tensor) -> torch.Tensor:
return torch.ops.et_vk.embedding_q4gsw.default(
self.weight, self.scales, EMB_GROUP, indices, True
)


def _write_embedding_goldens(
out_dir: str,
prefix: str,
weight: torch.Tensor,
scales: torch.Tensor,
group_size: int,
is_linear: bool,
) -> None:
for n in [EMB_MAXN, 8, 1]:
idx = (torch.arange(n, dtype=torch.long) * 7) % EMB_VOCAB
golden = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, is_linear
)
if is_linear:
nonlinear_golden = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, False
)
if torch.equal(golden, nonlinear_golden):
raise RuntimeError(
"emb_dyn_linear fixture does not distinguish nibble packing"
)
idx.detach().numpy().astype("<i8").tofile(
os.path.join(out_dir, f"{prefix}.S{n}.idx.bin")
)
golden.detach().numpy().astype("<f4").tofile(
os.path.join(out_dir, f"{prefix}.S{n}.golden.bin")
)
print(f" golden {prefix} N={n} (shape {tuple(golden.shape)})")


def _export_dynamic_embedding(out_dir: str) -> None:
from executorch.backends.webgpu.test.ops.embedding_q4gsw.test_embedding_q4gsw import (
from executorch.backends.webgpu.test.ops.test_embedding_q4gsw import (
_make_quantized_model,
_quant_params,
Shape,
Expand All@@ -346,18 +391,23 @@ def _export_dynamic_embedding(out_dir: str) -> None:
f.write(et.buffer)
print("Exported emb_dyn.pte")
weight, scales, group_size = _quant_params(qm)
for n in [EMB_MAXN, 8, 1]:
idx = (torch.arange(n, dtype=torch.long) * 7) % EMB_VOCAB
g = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, False
)
idx.detach().numpy().astype("<i8").tofile(
os.path.join(out_dir, f"emb_dyn.S{n}.idx.bin")
)
g.detach().numpy().astype("<f4").tofile(
os.path.join(out_dir, f"emb_dyn.S{n}.golden.bin")
)
print(f" golden emb_dyn N={n} (shape {tuple(g.shape)})")
_write_embedding_goldens(out_dir, "emb_dyn", weight, scales, group_size, False)

linear_model = _LinearPackedEmbedding().eval()
_export(
linear_model,
(idx_max,),
({0: n_dim},),
os.path.join(out_dir, "emb_dyn_linear.pte"),
)
_write_embedding_goldens(
out_dir,
"emb_dyn_linear",
linear_model.weight,
linear_model.scales,
EMB_GROUP,
True,
)


# Dynamic RoPE: xq/xk + freqs all share a dynamic seq-len S.
Expand Down
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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[executorch][webgpu] Test linear embedding dynamic reuse#21469

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Merged
JCNTH merged 1 commit into from
Jul 29, 2026
Merged

[executorch][webgpu] Test linear embedding dynamic reuse #21469

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25 changes: 17 additions & 8 deletions backends/webgpu/test/native/test_dynamic_shape.cpp
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -193,31 +193,31 @@ void check_sdpa(int s) {
constexpr int kEmbDim = 64;
// Run emb_dyn at N tokens on an already-loaded module (so it can be reused
// across N), and compare to the golden.
void run_embedding(Module& m, int n) {
const std::string b = g_dir + "/emb_dyn.S" + std::to_string(n) + ".";
void run_embedding(Module& m, int n, const char* prefix = "emb_dyn") {
const std::string b = g_dir + "/" + prefix + ".S" + std::to_string(n) + ".";
std::ifstream f(b + "idx.bin", std::ios::binary | std::ios::ate);
ASSERT_TRUE(f.good()) << "missing emb_dyn.S" << n;
ASSERT_TRUE(f.good()) << "missing " << prefix << ".S" << n;
const std::streamsize nb = f.tellg();
ASSERT_GE(nb, 0) << "missing emb_dyn.S" << n;
ASSERT_GE(nb, 0) << "missing " << prefix << ".S" << n;
f.seekg(0);
std::vector<int64_t> idx(static_cast<size_t>(nb) / sizeof(int64_t));
f.read(reinterpret_cast<char*>(idx.data()), nb);
ASSERT_EQ(idx.size(), static_cast<size_t>(n))
<< "wrong emb_dyn idx size S" << n;
<< "wrong " << prefix << " idx size S" << n;
auto golden = read_bin(b + "golden.bin");
auto t = make_tensor_ptr({n}, std::move(idx)); // int64 (Long) host input
auto r = m.forward({EValue(t)});
ASSERT_TRUE(r.ok() && !r.get().empty() && r.get()[0].isTensor())
<< "emb N=" << n
<< prefix << " N=" << n
<< " forward failed (err=" << (r.ok() ? 0 : (int)r.error()) << ")";
const auto& out = r.get()[0].toTensor();
const size_t numel = static_cast<size_t>(n) * kEmbDim;
ASSERT_EQ(static_cast<size_t>(out.numel()), numel)
<< "emb N=" << n << " output numel mismatch";
<< prefix << " N=" << n << " output numel mismatch";
std::vector<float> got(
out.const_data_ptr<float>(), out.const_data_ptr<float>() + numel);
const float e = max_err(got, golden);
EXPECT_LT(e, 5e-3f) << "emb_dyn N=" << n << " max_err=" << e;
EXPECT_LT(e, 5e-3f) << prefix << " N=" << n << " max_err=" << e;
}

void check_embedding(int n) {
Expand DownExpand Up@@ -459,6 +459,15 @@ TEST(DynamicShape, EmbeddingReusedGraph) {
}
}

// K3: linear-packed reuse must preserve nibble order across resizes.
TEST(DynamicShape, LinearPackedEmbeddingReusedGraph) {
Module m(g_dir + "/emb_dyn_linear.pte");
ASSERT_EQ(m.load_forward(), Error::Ok) << "load emb_dyn_linear.pte";
for (int n : {16, 8, 1, 16}) {
run_embedding(m, n, "emb_dyn_linear");
}
}

// L: dynamic RoPE (two outputs) at several seq-len S.
TEST(DynamicShape, Rope) {
for (int s : {16, 8, 1}) {
Expand Down
76 changes: 63 additions & 13 deletions backends/webgpu/test/ops/dynamic_shape/test_dynamic_shape_export.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -322,8 +322,53 @@ def cfg(s: int) -> "SdpaConfig":
EMB_MAXN = 16


class _LinearPackedEmbedding(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
packed = torch.arange(EMB_VOCAB * (EMB_DIM // 2), dtype=torch.int64).reshape(
EMB_VOCAB, EMB_DIM // 2
)
self.register_buffer("weight", (packed % 256).to(torch.uint8))
self.register_buffer("scales", torch.ones(EMB_VOCAB, EMB_DIM // EMB_GROUP))

def forward(self, indices: torch.Tensor) -> torch.Tensor:
return torch.ops.et_vk.embedding_q4gsw.default(
self.weight, self.scales, EMB_GROUP, indices, True
)


def _write_embedding_goldens(
out_dir: str,
prefix: str,
weight: torch.Tensor,
scales: torch.Tensor,
group_size: int,
is_linear: bool,
) -> None:
for n in [EMB_MAXN, 8, 1]:
idx = (torch.arange(n, dtype=torch.long) * 7) % EMB_VOCAB
golden = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, is_linear
)
if is_linear:
nonlinear_golden = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, False
)
if torch.equal(golden, nonlinear_golden):
raise RuntimeError(
"emb_dyn_linear fixture does not distinguish nibble packing"
)
idx.detach().numpy().astype("<i8").tofile(
os.path.join(out_dir, f"{prefix}.S{n}.idx.bin")
)
golden.detach().numpy().astype("<f4").tofile(
os.path.join(out_dir, f"{prefix}.S{n}.golden.bin")
)
print(f" golden {prefix} N={n} (shape {tuple(golden.shape)})")


def _export_dynamic_embedding(out_dir: str) -> None:
from executorch.backends.webgpu.test.ops.embedding_q4gsw.test_embedding_q4gsw import (
from executorch.backends.webgpu.test.ops.test_embedding_q4gsw import (
_make_quantized_model,
_quant_params,
Shape,
Expand All@@ -346,18 +391,23 @@ def _export_dynamic_embedding(out_dir: str) -> None:
f.write(et.buffer)
print("Exported emb_dyn.pte")
weight, scales, group_size = _quant_params(qm)
for n in [EMB_MAXN, 8, 1]:
idx = (torch.arange(n, dtype=torch.long) * 7) % EMB_VOCAB
g = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, False
)
idx.detach().numpy().astype("<i8").tofile(
os.path.join(out_dir, f"emb_dyn.S{n}.idx.bin")
)
g.detach().numpy().astype("<f4").tofile(
os.path.join(out_dir, f"emb_dyn.S{n}.golden.bin")
)
print(f" golden emb_dyn N={n} (shape {tuple(g.shape)})")
_write_embedding_goldens(out_dir, "emb_dyn", weight, scales, group_size, False)

linear_model = _LinearPackedEmbedding().eval()
_export(
linear_model,
(idx_max,),
({0: n_dim},),
os.path.join(out_dir, "emb_dyn_linear.pte"),
)
_write_embedding_goldens(
out_dir,
"emb_dyn_linear",
linear_model.weight,
linear_model.scales,
EMB_GROUP,
True,
)


# Dynamic RoPE: xq/xk + freqs all share a dynamic seq-len S.
Expand Down
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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[executorch][webgpu] Test linear embedding dynamic reuse#21469

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Merged
JCNTH merged 1 commit into from
Jul 29, 2026
Merged

[executorch][webgpu] Test linear embedding dynamic reuse #21469

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25 changes: 17 additions & 8 deletions backends/webgpu/test/native/test_dynamic_shape.cpp
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -193,31 +193,31 @@ void check_sdpa(int s) {
constexpr int kEmbDim = 64;
// Run emb_dyn at N tokens on an already-loaded module (so it can be reused
// across N), and compare to the golden.
void run_embedding(Module& m, int n) {
const std::string b = g_dir + "/emb_dyn.S" + std::to_string(n) + ".";
void run_embedding(Module& m, int n, const char* prefix = "emb_dyn") {
const std::string b = g_dir + "/" + prefix + ".S" + std::to_string(n) + ".";
std::ifstream f(b + "idx.bin", std::ios::binary | std::ios::ate);
ASSERT_TRUE(f.good()) << "missing emb_dyn.S" << n;
ASSERT_TRUE(f.good()) << "missing " << prefix << ".S" << n;
const std::streamsize nb = f.tellg();
ASSERT_GE(nb, 0) << "missing emb_dyn.S" << n;
ASSERT_GE(nb, 0) << "missing " << prefix << ".S" << n;
f.seekg(0);
std::vector<int64_t> idx(static_cast<size_t>(nb) / sizeof(int64_t));
f.read(reinterpret_cast<char*>(idx.data()), nb);
ASSERT_EQ(idx.size(), static_cast<size_t>(n))
<< "wrong emb_dyn idx size S" << n;
<< "wrong " << prefix << " idx size S" << n;
auto golden = read_bin(b + "golden.bin");
auto t = make_tensor_ptr({n}, std::move(idx)); // int64 (Long) host input
auto r = m.forward({EValue(t)});
ASSERT_TRUE(r.ok() && !r.get().empty() && r.get()[0].isTensor())
<< "emb N=" << n
<< prefix << " N=" << n
<< " forward failed (err=" << (r.ok() ? 0 : (int)r.error()) << ")";
const auto& out = r.get()[0].toTensor();
const size_t numel = static_cast<size_t>(n) * kEmbDim;
ASSERT_EQ(static_cast<size_t>(out.numel()), numel)
<< "emb N=" << n << " output numel mismatch";
<< prefix << " N=" << n << " output numel mismatch";
std::vector<float> got(
out.const_data_ptr<float>(), out.const_data_ptr<float>() + numel);
const float e = max_err(got, golden);
EXPECT_LT(e, 5e-3f) << "emb_dyn N=" << n << " max_err=" << e;
EXPECT_LT(e, 5e-3f) << prefix << " N=" << n << " max_err=" << e;
}

void check_embedding(int n) {
Expand DownExpand Up@@ -459,6 +459,15 @@ TEST(DynamicShape, EmbeddingReusedGraph) {
}
}

// K3: linear-packed reuse must preserve nibble order across resizes.
TEST(DynamicShape, LinearPackedEmbeddingReusedGraph) {
Module m(g_dir + "/emb_dyn_linear.pte");
ASSERT_EQ(m.load_forward(), Error::Ok) << "load emb_dyn_linear.pte";
for (int n : {16, 8, 1, 16}) {
run_embedding(m, n, "emb_dyn_linear");
}
}

// L: dynamic RoPE (two outputs) at several seq-len S.
TEST(DynamicShape, Rope) {
for (int s : {16, 8, 1}) {
Expand Down
76 changes: 63 additions & 13 deletions backends/webgpu/test/ops/dynamic_shape/test_dynamic_shape_export.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -322,8 +322,53 @@ def cfg(s: int) -> "SdpaConfig":
EMB_MAXN = 16


class _LinearPackedEmbedding(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
packed = torch.arange(EMB_VOCAB * (EMB_DIM // 2), dtype=torch.int64).reshape(
EMB_VOCAB, EMB_DIM // 2
)
self.register_buffer("weight", (packed % 256).to(torch.uint8))
self.register_buffer("scales", torch.ones(EMB_VOCAB, EMB_DIM // EMB_GROUP))

def forward(self, indices: torch.Tensor) -> torch.Tensor:
return torch.ops.et_vk.embedding_q4gsw.default(
self.weight, self.scales, EMB_GROUP, indices, True
)


def _write_embedding_goldens(
out_dir: str,
prefix: str,
weight: torch.Tensor,
scales: torch.Tensor,
group_size: int,
is_linear: bool,
) -> None:
for n in [EMB_MAXN, 8, 1]:
idx = (torch.arange(n, dtype=torch.long) * 7) % EMB_VOCAB
golden = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, is_linear
)
if is_linear:
nonlinear_golden = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, False
)
if torch.equal(golden, nonlinear_golden):
raise RuntimeError(
"emb_dyn_linear fixture does not distinguish nibble packing"
)
idx.detach().numpy().astype("<i8").tofile(
os.path.join(out_dir, f"{prefix}.S{n}.idx.bin")
)
golden.detach().numpy().astype("<f4").tofile(
os.path.join(out_dir, f"{prefix}.S{n}.golden.bin")
)
print(f" golden {prefix} N={n} (shape {tuple(golden.shape)})")


def _export_dynamic_embedding(out_dir: str) -> None:
from executorch.backends.webgpu.test.ops.embedding_q4gsw.test_embedding_q4gsw import (
from executorch.backends.webgpu.test.ops.test_embedding_q4gsw import (
_make_quantized_model,
_quant_params,
Shape,
Expand All@@ -346,18 +391,23 @@ def _export_dynamic_embedding(out_dir: str) -> None:
f.write(et.buffer)
print("Exported emb_dyn.pte")
weight, scales, group_size = _quant_params(qm)
for n in [EMB_MAXN, 8, 1]:
idx = (torch.arange(n, dtype=torch.long) * 7) % EMB_VOCAB
g = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, False
)
idx.detach().numpy().astype("<i8").tofile(
os.path.join(out_dir, f"emb_dyn.S{n}.idx.bin")
)
g.detach().numpy().astype("<f4").tofile(
os.path.join(out_dir, f"emb_dyn.S{n}.golden.bin")
)
print(f" golden emb_dyn N={n} (shape {tuple(g.shape)})")
_write_embedding_goldens(out_dir, "emb_dyn", weight, scales, group_size, False)

linear_model = _LinearPackedEmbedding().eval()
_export(
linear_model,
(idx_max,),
({0: n_dim},),
os.path.join(out_dir, "emb_dyn_linear.pte"),
)
_write_embedding_goldens(
out_dir,
"emb_dyn_linear",
linear_model.weight,
linear_model.scales,
EMB_GROUP,
True,
)


# Dynamic RoPE: xq/xk + freqs all share a dynamic seq-len S.
Expand Down
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
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[executorch][webgpu] Test linear embedding dynamic reuse#21469

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Merged
JCNTH merged 1 commit into from
Jul 29, 2026
Merged

[executorch][webgpu] Test linear embedding dynamic reuse #21469

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25 changes: 17 additions & 8 deletions backends/webgpu/test/native/test_dynamic_shape.cpp
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -193,31 +193,31 @@ void check_sdpa(int s) {
constexpr int kEmbDim = 64;
// Run emb_dyn at N tokens on an already-loaded module (so it can be reused
// across N), and compare to the golden.
void run_embedding(Module& m, int n) {
const std::string b = g_dir + "/emb_dyn.S" + std::to_string(n) + ".";
void run_embedding(Module& m, int n, const char* prefix = "emb_dyn") {
const std::string b = g_dir + "/" + prefix + ".S" + std::to_string(n) + ".";
std::ifstream f(b + "idx.bin", std::ios::binary | std::ios::ate);
ASSERT_TRUE(f.good()) << "missing emb_dyn.S" << n;
ASSERT_TRUE(f.good()) << "missing " << prefix << ".S" << n;
const std::streamsize nb = f.tellg();
ASSERT_GE(nb, 0) << "missing emb_dyn.S" << n;
ASSERT_GE(nb, 0) << "missing " << prefix << ".S" << n;
f.seekg(0);
std::vector<int64_t> idx(static_cast<size_t>(nb) / sizeof(int64_t));
f.read(reinterpret_cast<char*>(idx.data()), nb);
ASSERT_EQ(idx.size(), static_cast<size_t>(n))
<< "wrong emb_dyn idx size S" << n;
<< "wrong " << prefix << " idx size S" << n;
auto golden = read_bin(b + "golden.bin");
auto t = make_tensor_ptr({n}, std::move(idx)); // int64 (Long) host input
auto r = m.forward({EValue(t)});
ASSERT_TRUE(r.ok() && !r.get().empty() && r.get()[0].isTensor())
<< "emb N=" << n
<< prefix << " N=" << n
<< " forward failed (err=" << (r.ok() ? 0 : (int)r.error()) << ")";
const auto& out = r.get()[0].toTensor();
const size_t numel = static_cast<size_t>(n) * kEmbDim;
ASSERT_EQ(static_cast<size_t>(out.numel()), numel)
<< "emb N=" << n << " output numel mismatch";
<< prefix << " N=" << n << " output numel mismatch";
std::vector<float> got(
out.const_data_ptr<float>(), out.const_data_ptr<float>() + numel);
const float e = max_err(got, golden);
EXPECT_LT(e, 5e-3f) << "emb_dyn N=" << n << " max_err=" << e;
EXPECT_LT(e, 5e-3f) << prefix << " N=" << n << " max_err=" << e;
}

void check_embedding(int n) {
Expand DownExpand Up@@ -459,6 +459,15 @@ TEST(DynamicShape, EmbeddingReusedGraph) {
}
}

// K3: linear-packed reuse must preserve nibble order across resizes.
TEST(DynamicShape, LinearPackedEmbeddingReusedGraph) {
Module m(g_dir + "/emb_dyn_linear.pte");
ASSERT_EQ(m.load_forward(), Error::Ok) << "load emb_dyn_linear.pte";
for (int n : {16, 8, 1, 16}) {
run_embedding(m, n, "emb_dyn_linear");
}
}

// L: dynamic RoPE (two outputs) at several seq-len S.
TEST(DynamicShape, Rope) {
for (int s : {16, 8, 1}) {
Expand Down
76 changes: 63 additions & 13 deletions backends/webgpu/test/ops/dynamic_shape/test_dynamic_shape_export.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -322,8 +322,53 @@ def cfg(s: int) -> "SdpaConfig":
EMB_MAXN = 16


class _LinearPackedEmbedding(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
packed = torch.arange(EMB_VOCAB * (EMB_DIM // 2), dtype=torch.int64).reshape(
EMB_VOCAB, EMB_DIM // 2
)
self.register_buffer("weight", (packed % 256).to(torch.uint8))
self.register_buffer("scales", torch.ones(EMB_VOCAB, EMB_DIM // EMB_GROUP))

def forward(self, indices: torch.Tensor) -> torch.Tensor:
return torch.ops.et_vk.embedding_q4gsw.default(
self.weight, self.scales, EMB_GROUP, indices, True
)


def _write_embedding_goldens(
out_dir: str,
prefix: str,
weight: torch.Tensor,
scales: torch.Tensor,
group_size: int,
is_linear: bool,
) -> None:
for n in [EMB_MAXN, 8, 1]:
idx = (torch.arange(n, dtype=torch.long) * 7) % EMB_VOCAB
golden = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, is_linear
)
if is_linear:
nonlinear_golden = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, False
)
if torch.equal(golden, nonlinear_golden):
raise RuntimeError(
"emb_dyn_linear fixture does not distinguish nibble packing"
)
idx.detach().numpy().astype("<i8").tofile(
os.path.join(out_dir, f"{prefix}.S{n}.idx.bin")
)
golden.detach().numpy().astype("<f4").tofile(
os.path.join(out_dir, f"{prefix}.S{n}.golden.bin")
)
print(f" golden {prefix} N={n} (shape {tuple(golden.shape)})")


def _export_dynamic_embedding(out_dir: str) -> None:
from executorch.backends.webgpu.test.ops.embedding_q4gsw.test_embedding_q4gsw import (
from executorch.backends.webgpu.test.ops.test_embedding_q4gsw import (
_make_quantized_model,
_quant_params,
Shape,
Expand All@@ -346,18 +391,23 @@ def _export_dynamic_embedding(out_dir: str) -> None:
f.write(et.buffer)
print("Exported emb_dyn.pte")
weight, scales, group_size = _quant_params(qm)
for n in [EMB_MAXN, 8, 1]:
idx = (torch.arange(n, dtype=torch.long) * 7) % EMB_VOCAB
g = torch.ops.et_vk.embedding_q4gsw.default(
weight, scales, group_size, idx, False
)
idx.detach().numpy().astype("<i8").tofile(
os.path.join(out_dir, f"emb_dyn.S{n}.idx.bin")
)
g.detach().numpy().astype("<f4").tofile(
os.path.join(out_dir, f"emb_dyn.S{n}.golden.bin")
)
print(f" golden emb_dyn N={n} (shape {tuple(g.shape)})")
_write_embedding_goldens(out_dir, "emb_dyn", weight, scales, group_size, False)

linear_model = _LinearPackedEmbedding().eval()
_export(
linear_model,
(idx_max,),
({0: n_dim},),
os.path.join(out_dir, "emb_dyn_linear.pte"),
)
_write_embedding_goldens(
out_dir,
"emb_dyn_linear",
linear_model.weight,
linear_model.scales,
EMB_GROUP,
True,
)


# Dynamic RoPE: xq/xk + freqs all share a dynamic seq-len S.
Expand Down
Loading