[Executorch] Add simd path for op quantize#15768

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SS-JIA merged 3 commits into from
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[Executorch] Add simd path for op quantize #15768

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848d476
[Executorch][llm] Fix flakyness of quantized sdpa test
kimishpatel Nov 10, 2025
444ee9d
[Executorch] parallelize op_choose_qparams
kimishpatel Nov 10, 2025
414aa6f
[Executorch] Add simd path for op quantize
kimishpatel Nov 10, 2025
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1 change: 1 addition & 0 deletions extension/llm/custom_ops/TARGETS
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Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -60,5 +60,6 @@ runtime.python_test(
],
deps = [
"//caffe2:torch",
"//executorch/extension/pybindings:portable_lib",
],
)
6 changes: 6 additions & 0 deletions extension/llm/custom_ops/test_quantized_sdpa.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -12,6 +12,7 @@
import torch.nn.functional as F

from executorch.extension.llm.custom_ops import custom_ops # noqa
from executorch.extension.pybindings.portable_lib import _unsafe_reset_threadpool


def is_fbcode():
Expand DownExpand Up@@ -40,6 +41,11 @@ def setUp(self):
self.q_shape = None
self.kv_shape = None
self.is_seq_at_dim_2 = True
# For some reason 4 threads doesnt work
# This setting is needed to make this test not flaky due to OMP
# error of "OMP: Error #131: Thread identifier invalid"
# Not clear why that happens but having smaller threadpool resolves it
_unsafe_reset_threadpool(3)

def _scale_tensor(self, tensor, min_value, max_value, scale=True):
normalized_tensor = (tensor - tensor.min()) / (tensor.max() - tensor.min())
Expand Down
48 changes: 37 additions & 11 deletions kernels/quantized/cpu/op_choose_qparams.cpp
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Original file line numberDiff line numberDiff line change
Expand Up@@ -8,6 +8,7 @@

#include <executorch/kernels/portable/cpu/vec_ops.h>
#include <executorch/runtime/kernel/kernel_includes.h>
#include <executorch/runtime/kernel/thread_parallel_interface.h>
#include <algorithm>
#include <cinttypes>
#include <cmath>
Expand DownExpand Up@@ -202,17 +203,42 @@ void choose_qparams_per_token(
num_tokens *= input.size(i);
}
auto token_dim_size = input.size(input.dim() - 1);
for (auto i = 0; i < num_tokens; i++) {
// vec_minf uses std::min_element. Check if it actually
// gets vectorized.
float min = torch::executor::vec_minf(x_fp32, token_dim_size);
float max = torch::executor::vec_maxf(x_fp32, token_dim_size);
double scale;
int32_t zero_point;
calculate_scale_and_zero_point(min, max, qmin, qmax, scale, zero_point);
scale_out.mutable_data_ptr<double>()[i] = scale;
zero_point_out.mutable_data_ptr<int64_t>()[i] = zero_point;
x_fp32 += token_dim_size;

const int64_t total_elements = num_tokens * token_dim_size;
constexpr int64_t MIN_ELEMENTS_FOR_PARALLEL = 512;
const bool use_parallel = total_elements >= MIN_ELEMENTS_FOR_PARALLEL;

if (use_parallel) {
auto* scale_data = scale_out.mutable_data_ptr<double>();
auto* zero_point_data = zero_point_out.mutable_data_ptr<int64_t>();

::executorch::extension::parallel_for(
0, num_tokens, 1, [&](const int64_t begin, const int64_t end) {
for (int64_t i = begin; i < end; i++) {
const float* token_data = x_fp32 + i * token_dim_size;
float min = torch::executor::vec_minf(token_data, token_dim_size);
float max = torch::executor::vec_maxf(token_data, token_dim_size);
double scale;
int32_t zero_point;
calculate_scale_and_zero_point(
min, max, qmin, qmax, scale, zero_point);
scale_data[i] = scale;
zero_point_data[i] = zero_point;
}
});
} else {
for (auto i = 0; i < num_tokens; i++) {
// vec_minf uses std::min_element. Check if it actually
// gets vectorized.
float min = torch::executor::vec_minf(x_fp32, token_dim_size);
float max = torch::executor::vec_maxf(x_fp32, token_dim_size);
double scale;
int32_t zero_point;
calculate_scale_and_zero_point(min, max, qmin, qmax, scale, zero_point);
scale_out.mutable_data_ptr<double>()[i] = scale;
zero_point_out.mutable_data_ptr<int64_t>()[i] = zero_point;
x_fp32 += token_dim_size;
}
}
}
} // namespace
Expand Down
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})();
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try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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[Executorch] Add simd path for op quantize#15768

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SS-JIA
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Nov 14, 2025
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[Executorch] Add simd path for op quantize
#15768
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848d476

[Executorch][llm] Fix flakyness of quantized sdpa test


kimishpatel Nov 10, 2025
444ee9d

[Executorch] parallelize op_choose_qparams


kimishpatel Nov 10, 2025
414aa6f

[Executorch] Add simd path for op quantize


kimishpatel Nov 10, 2025

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1 change: 1 addition & 0 deletions
extension/llm/custom_ops/TARGETS

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -60,5 +60,6 @@ runtime.python_test(
 ],
 deps = [
 "//caffe2:torch",
 "//executorch/extension/pybindings:portable_lib",
 ],
)

6 changes: 6 additions & 0 deletions
extension/llm/custom_ops/test_quantized_sdpa.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -12,6 +12,7 @@
import torch.nn.functional as F

from executorch.extension.llm.custom_ops import custom_ops # noqa
from executorch.extension.pybindings.portable_lib import _unsafe_reset_threadpool


def is_fbcode():
Expand DownExpand Up@@ -40,6 +41,11 @@ def setUp(self):
 self.q_shape = None
 self.kv_shape = None
 self.is_seq_at_dim_2 = True
 # For some reason 4 threads doesnt work
 # This setting is needed to make this test not flaky due to OMP
 # error of "OMP: Error #131: Thread identifier invalid"
 # Not clear why that happens but having smaller threadpool resolves it
 _unsafe_reset_threadpool(3)

 def _scale_tensor(self, tensor, min_value, max_value, scale=True):
 normalized_tensor = (tensor - tensor.min()) / (tensor.max() - tensor.min())
Expand Down

48 changes: 37 additions & 11 deletions
kernels/quantized/cpu/op_choose_qparams.cpp

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Original file line numberDiff line numberDiff line change
Expand Up@@ -8,6 +8,7 @@

#include <executorch/kernels/portable/cpu/vec_ops.h>
#include <executorch/runtime/kernel/kernel_includes.h>
#include <executorch/runtime/kernel/thread_parallel_interface.h>
#include <algorithm>
#include <cinttypes>
#include <cmath>
Expand DownExpand Up@@ -202,17 +203,42 @@ void choose_qparams_per_token(
 num_tokens *= input.size(i);
 }
 auto token_dim_size = input.size(input.dim() - 1);
 for (auto i = 0; i < num_tokens; i++) {
 // vec_minf uses std::min_element. Check if it actually
 // gets vectorized.
 float min = torch::executor::vec_minf(x_fp32, token_dim_size);
 float max = torch::executor::vec_maxf(x_fp32, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(min, max, qmin, qmax, scale, zero_point);
 scale_out.mutable_data_ptr<double>()[i] = scale;
 zero_point_out.mutable_data_ptr<int64_t>()[i] = zero_point;
 x_fp32 += token_dim_size;

 const int64_t total_elements = num_tokens * token_dim_size;
 constexpr int64_t MIN_ELEMENTS_FOR_PARALLEL = 512;
 const bool use_parallel = total_elements >= MIN_ELEMENTS_FOR_PARALLEL;

 if (use_parallel) {
 auto* scale_data = scale_out.mutable_data_ptr<double>();
 auto* zero_point_data = zero_point_out.mutable_data_ptr<int64_t>();

 ::executorch::extension::parallel_for(
 0, num_tokens, 1, [&](const int64_t begin, const int64_t end) {
 for (int64_t i = begin; i < end; i++) {
 const float* token_data = x_fp32 + i * token_dim_size;
 float min = torch::executor::vec_minf(token_data, token_dim_size);
 float max = torch::executor::vec_maxf(token_data, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(
 min, max, qmin, qmax, scale, zero_point);
 scale_data[i] = scale;
 zero_point_data[i] = zero_point;
 }
 });
 } else {
 for (auto i = 0; i < num_tokens; i++) {
 // vec_minf uses std::min_element. Check if it actually
 // gets vectorized.
 float min = torch::executor::vec_minf(x_fp32, token_dim_size);
 float max = torch::executor::vec_maxf(x_fp32, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(min, max, qmin, qmax, scale, zero_point);
 scale_out.mutable_data_ptr<double>()[i] = scale;
 zero_point_out.mutable_data_ptr<int64_t>()[i] = zero_point;
 x_fp32 += token_dim_size;
 }
 }
}
} // namespace
Expand Down
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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");
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(function(){
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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[Executorch] Add simd path for op quantize#15768

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 Merged

SS-JIA
merged 3 commits into

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Nov 14, 2025
 Merged


[Executorch] Add simd path for op quantize
#15768
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848d476

[Executorch][llm] Fix flakyness of quantized sdpa test


kimishpatel Nov 10, 2025
444ee9d

[Executorch] parallelize op_choose_qparams


kimishpatel Nov 10, 2025
414aa6f

[Executorch] Add simd path for op quantize


kimishpatel Nov 10, 2025

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1 change: 1 addition & 0 deletions
extension/llm/custom_ops/TARGETS

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -60,5 +60,6 @@ runtime.python_test(
 ],
 deps = [
 "//caffe2:torch",
 "//executorch/extension/pybindings:portable_lib",
 ],
)

6 changes: 6 additions & 0 deletions
extension/llm/custom_ops/test_quantized_sdpa.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -12,6 +12,7 @@
import torch.nn.functional as F

from executorch.extension.llm.custom_ops import custom_ops # noqa
from executorch.extension.pybindings.portable_lib import _unsafe_reset_threadpool


def is_fbcode():
Expand DownExpand Up@@ -40,6 +41,11 @@ def setUp(self):
 self.q_shape = None
 self.kv_shape = None
 self.is_seq_at_dim_2 = True
 # For some reason 4 threads doesnt work
 # This setting is needed to make this test not flaky due to OMP
 # error of "OMP: Error #131: Thread identifier invalid"
 # Not clear why that happens but having smaller threadpool resolves it
 _unsafe_reset_threadpool(3)

 def _scale_tensor(self, tensor, min_value, max_value, scale=True):
 normalized_tensor = (tensor - tensor.min()) / (tensor.max() - tensor.min())
Expand Down

48 changes: 37 additions & 11 deletions
kernels/quantized/cpu/op_choose_qparams.cpp

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Original file line numberDiff line numberDiff line change
Expand Up@@ -8,6 +8,7 @@

#include <executorch/kernels/portable/cpu/vec_ops.h>
#include <executorch/runtime/kernel/kernel_includes.h>
#include <executorch/runtime/kernel/thread_parallel_interface.h>
#include <algorithm>
#include <cinttypes>
#include <cmath>
Expand DownExpand Up@@ -202,17 +203,42 @@ void choose_qparams_per_token(
 num_tokens *= input.size(i);
 }
 auto token_dim_size = input.size(input.dim() - 1);
 for (auto i = 0; i < num_tokens; i++) {
 // vec_minf uses std::min_element. Check if it actually
 // gets vectorized.
 float min = torch::executor::vec_minf(x_fp32, token_dim_size);
 float max = torch::executor::vec_maxf(x_fp32, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(min, max, qmin, qmax, scale, zero_point);
 scale_out.mutable_data_ptr<double>()[i] = scale;
 zero_point_out.mutable_data_ptr<int64_t>()[i] = zero_point;
 x_fp32 += token_dim_size;

 const int64_t total_elements = num_tokens * token_dim_size;
 constexpr int64_t MIN_ELEMENTS_FOR_PARALLEL = 512;
 const bool use_parallel = total_elements >= MIN_ELEMENTS_FOR_PARALLEL;

 if (use_parallel) {
 auto* scale_data = scale_out.mutable_data_ptr<double>();
 auto* zero_point_data = zero_point_out.mutable_data_ptr<int64_t>();

 ::executorch::extension::parallel_for(
 0, num_tokens, 1, [&](const int64_t begin, const int64_t end) {
 for (int64_t i = begin; i < end; i++) {
 const float* token_data = x_fp32 + i * token_dim_size;
 float min = torch::executor::vec_minf(token_data, token_dim_size);
 float max = torch::executor::vec_maxf(token_data, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(
 min, max, qmin, qmax, scale, zero_point);
 scale_data[i] = scale;
 zero_point_data[i] = zero_point;
 }
 });
 } else {
 for (auto i = 0; i < num_tokens; i++) {
 // vec_minf uses std::min_element. Check if it actually
 // gets vectorized.
 float min = torch::executor::vec_minf(x_fp32, token_dim_size);
 float max = torch::executor::vec_maxf(x_fp32, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(min, max, qmin, qmax, scale, zero_point);
 scale_out.mutable_data_ptr<double>()[i] = scale;
 zero_point_out.mutable_data_ptr<int64_t>()[i] = zero_point;
 x_fp32 += token_dim_size;
 }
 }
}
} // namespace
Expand Down
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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 > 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] Add simd path for op quantize#15768

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 Merged

SS-JIA
merged 3 commits into

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Nov 14, 2025
 Merged


[Executorch] Add simd path for op quantize
#15768
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848d476

[Executorch][llm] Fix flakyness of quantized sdpa test


kimishpatel Nov 10, 2025
444ee9d

[Executorch] parallelize op_choose_qparams


kimishpatel Nov 10, 2025
414aa6f

[Executorch] Add simd path for op quantize


kimishpatel Nov 10, 2025

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1 change: 1 addition & 0 deletions
extension/llm/custom_ops/TARGETS

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -60,5 +60,6 @@ runtime.python_test(
 ],
 deps = [
 "//caffe2:torch",
 "//executorch/extension/pybindings:portable_lib",
 ],
)

6 changes: 6 additions & 0 deletions
extension/llm/custom_ops/test_quantized_sdpa.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -12,6 +12,7 @@
import torch.nn.functional as F

from executorch.extension.llm.custom_ops import custom_ops # noqa
from executorch.extension.pybindings.portable_lib import _unsafe_reset_threadpool


def is_fbcode():
Expand DownExpand Up@@ -40,6 +41,11 @@ def setUp(self):
 self.q_shape = None
 self.kv_shape = None
 self.is_seq_at_dim_2 = True
 # For some reason 4 threads doesnt work
 # This setting is needed to make this test not flaky due to OMP
 # error of "OMP: Error #131: Thread identifier invalid"
 # Not clear why that happens but having smaller threadpool resolves it
 _unsafe_reset_threadpool(3)

 def _scale_tensor(self, tensor, min_value, max_value, scale=True):
 normalized_tensor = (tensor - tensor.min()) / (tensor.max() - tensor.min())
Expand Down

48 changes: 37 additions & 11 deletions
kernels/quantized/cpu/op_choose_qparams.cpp

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,6 +8,7 @@

#include <executorch/kernels/portable/cpu/vec_ops.h>
#include <executorch/runtime/kernel/kernel_includes.h>
#include <executorch/runtime/kernel/thread_parallel_interface.h>
#include <algorithm>
#include <cinttypes>
#include <cmath>
Expand DownExpand Up@@ -202,17 +203,42 @@ void choose_qparams_per_token(
 num_tokens *= input.size(i);
 }
 auto token_dim_size = input.size(input.dim() - 1);
 for (auto i = 0; i < num_tokens; i++) {
 // vec_minf uses std::min_element. Check if it actually
 // gets vectorized.
 float min = torch::executor::vec_minf(x_fp32, token_dim_size);
 float max = torch::executor::vec_maxf(x_fp32, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(min, max, qmin, qmax, scale, zero_point);
 scale_out.mutable_data_ptr<double>()[i] = scale;
 zero_point_out.mutable_data_ptr<int64_t>()[i] = zero_point;
 x_fp32 += token_dim_size;

 const int64_t total_elements = num_tokens * token_dim_size;
 constexpr int64_t MIN_ELEMENTS_FOR_PARALLEL = 512;
 const bool use_parallel = total_elements >= MIN_ELEMENTS_FOR_PARALLEL;

 if (use_parallel) {
 auto* scale_data = scale_out.mutable_data_ptr<double>();
 auto* zero_point_data = zero_point_out.mutable_data_ptr<int64_t>();

 ::executorch::extension::parallel_for(
 0, num_tokens, 1, [&](const int64_t begin, const int64_t end) {
 for (int64_t i = begin; i < end; i++) {
 const float* token_data = x_fp32 + i * token_dim_size;
 float min = torch::executor::vec_minf(token_data, token_dim_size);
 float max = torch::executor::vec_maxf(token_data, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(
 min, max, qmin, qmax, scale, zero_point);
 scale_data[i] = scale;
 zero_point_data[i] = zero_point;
 }
 });
 } else {
 for (auto i = 0; i < num_tokens; i++) {
 // vec_minf uses std::min_element. Check if it actually
 // gets vectorized.
 float min = torch::executor::vec_minf(x_fp32, token_dim_size);
 float max = torch::executor::vec_maxf(x_fp32, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(min, max, qmin, qmax, scale, zero_point);
 scale_out.mutable_data_ptr<double>()[i] = scale;
 zero_point_out.mutable_data_ptr<int64_t>()[i] = zero_point;
 x_fp32 += token_dim_size;
 }
 }
}
} // namespace
Expand Down
Loading
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] Add simd path for op quantize
#15768
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848d476

[Executorch][llm] Fix flakyness of quantized sdpa test


kimishpatel Nov 10, 2025
444ee9d

[Executorch] parallelize op_choose_qparams


kimishpatel Nov 10, 2025
414aa6f

[Executorch] Add simd path for op quantize


kimishpatel Nov 10, 2025

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1 change: 1 addition & 0 deletions
extension/llm/custom_ops/TARGETS

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -60,5 +60,6 @@ runtime.python_test(
 ],
 deps = [
 "//caffe2:torch",
 "//executorch/extension/pybindings:portable_lib",
 ],
)

6 changes: 6 additions & 0 deletions
extension/llm/custom_ops/test_quantized_sdpa.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -12,6 +12,7 @@
import torch.nn.functional as F

from executorch.extension.llm.custom_ops import custom_ops # noqa
from executorch.extension.pybindings.portable_lib import _unsafe_reset_threadpool


def is_fbcode():
Expand DownExpand Up@@ -40,6 +41,11 @@ def setUp(self):
 self.q_shape = None
 self.kv_shape = None
 self.is_seq_at_dim_2 = True
 # For some reason 4 threads doesnt work
 # This setting is needed to make this test not flaky due to OMP
 # error of "OMP: Error #131: Thread identifier invalid"
 # Not clear why that happens but having smaller threadpool resolves it
 _unsafe_reset_threadpool(3)

 def _scale_tensor(self, tensor, min_value, max_value, scale=True):
 normalized_tensor = (tensor - tensor.min()) / (tensor.max() - tensor.min())
Expand Down

48 changes: 37 additions & 11 deletions
kernels/quantized/cpu/op_choose_qparams.cpp

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,6 +8,7 @@

#include <executorch/kernels/portable/cpu/vec_ops.h>
#include <executorch/runtime/kernel/kernel_includes.h>
#include <executorch/runtime/kernel/thread_parallel_interface.h>
#include <algorithm>
#include <cinttypes>
#include <cmath>
Expand DownExpand Up@@ -202,17 +203,42 @@ void choose_qparams_per_token(
 num_tokens *= input.size(i);
 }
 auto token_dim_size = input.size(input.dim() - 1);
 for (auto i = 0; i < num_tokens; i++) {
 // vec_minf uses std::min_element. Check if it actually
 // gets vectorized.
 float min = torch::executor::vec_minf(x_fp32, token_dim_size);
 float max = torch::executor::vec_maxf(x_fp32, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(min, max, qmin, qmax, scale, zero_point);
 scale_out.mutable_data_ptr<double>()[i] = scale;
 zero_point_out.mutable_data_ptr<int64_t>()[i] = zero_point;
 x_fp32 += token_dim_size;

 const int64_t total_elements = num_tokens * token_dim_size;
 constexpr int64_t MIN_ELEMENTS_FOR_PARALLEL = 512;
 const bool use_parallel = total_elements >= MIN_ELEMENTS_FOR_PARALLEL;

 if (use_parallel) {
 auto* scale_data = scale_out.mutable_data_ptr<double>();
 auto* zero_point_data = zero_point_out.mutable_data_ptr<int64_t>();

 ::executorch::extension::parallel_for(
 0, num_tokens, 1, [&](const int64_t begin, const int64_t end) {
 for (int64_t i = begin; i < end; i++) {
 const float* token_data = x_fp32 + i * token_dim_size;
 float min = torch::executor::vec_minf(token_data, token_dim_size);
 float max = torch::executor::vec_maxf(token_data, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(
 min, max, qmin, qmax, scale, zero_point);
 scale_data[i] = scale;
 zero_point_data[i] = zero_point;
 }
 });
 } else {
 for (auto i = 0; i < num_tokens; i++) {
 // vec_minf uses std::min_element. Check if it actually
 // gets vectorized.
 float min = torch::executor::vec_minf(x_fp32, token_dim_size);
 float max = torch::executor::vec_maxf(x_fp32, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(min, max, qmin, qmax, scale, zero_point);
 scale_out.mutable_data_ptr<double>()[i] = scale;
 zero_point_out.mutable_data_ptr<int64_t>()[i] = zero_point;
 x_fp32 += token_dim_size;
 }
 }
}
} // namespace
Expand Down
Loading
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] Add simd path for op quantize#15768

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 Merged

SS-JIA
merged 3 commits into

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Nov 14, 2025
 Merged


[Executorch] Add simd path for op quantize
#15768
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848d476

[Executorch][llm] Fix flakyness of quantized sdpa test


kimishpatel Nov 10, 2025
444ee9d

[Executorch] parallelize op_choose_qparams


kimishpatel Nov 10, 2025
414aa6f

[Executorch] Add simd path for op quantize


kimishpatel Nov 10, 2025

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1 change: 1 addition & 0 deletions
extension/llm/custom_ops/TARGETS

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -60,5 +60,6 @@ runtime.python_test(
 ],
 deps = [
 "//caffe2:torch",
 "//executorch/extension/pybindings:portable_lib",
 ],
)

6 changes: 6 additions & 0 deletions
extension/llm/custom_ops/test_quantized_sdpa.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -12,6 +12,7 @@
import torch.nn.functional as F

from executorch.extension.llm.custom_ops import custom_ops # noqa
from executorch.extension.pybindings.portable_lib import _unsafe_reset_threadpool


def is_fbcode():
Expand DownExpand Up@@ -40,6 +41,11 @@ def setUp(self):
 self.q_shape = None
 self.kv_shape = None
 self.is_seq_at_dim_2 = True
 # For some reason 4 threads doesnt work
 # This setting is needed to make this test not flaky due to OMP
 # error of "OMP: Error #131: Thread identifier invalid"
 # Not clear why that happens but having smaller threadpool resolves it
 _unsafe_reset_threadpool(3)

 def _scale_tensor(self, tensor, min_value, max_value, scale=True):
 normalized_tensor = (tensor - tensor.min()) / (tensor.max() - tensor.min())
Expand Down

48 changes: 37 additions & 11 deletions
kernels/quantized/cpu/op_choose_qparams.cpp

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,6 +8,7 @@

#include <executorch/kernels/portable/cpu/vec_ops.h>
#include <executorch/runtime/kernel/kernel_includes.h>
#include <executorch/runtime/kernel/thread_parallel_interface.h>
#include <algorithm>
#include <cinttypes>
#include <cmath>
Expand DownExpand Up@@ -202,17 +203,42 @@ void choose_qparams_per_token(
 num_tokens *= input.size(i);
 }
 auto token_dim_size = input.size(input.dim() - 1);
 for (auto i = 0; i < num_tokens; i++) {
 // vec_minf uses std::min_element. Check if it actually
 // gets vectorized.
 float min = torch::executor::vec_minf(x_fp32, token_dim_size);
 float max = torch::executor::vec_maxf(x_fp32, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(min, max, qmin, qmax, scale, zero_point);
 scale_out.mutable_data_ptr<double>()[i] = scale;
 zero_point_out.mutable_data_ptr<int64_t>()[i] = zero_point;
 x_fp32 += token_dim_size;

 const int64_t total_elements = num_tokens * token_dim_size;
 constexpr int64_t MIN_ELEMENTS_FOR_PARALLEL = 512;
 const bool use_parallel = total_elements >= MIN_ELEMENTS_FOR_PARALLEL;

 if (use_parallel) {
 auto* scale_data = scale_out.mutable_data_ptr<double>();
 auto* zero_point_data = zero_point_out.mutable_data_ptr<int64_t>();

 ::executorch::extension::parallel_for(
 0, num_tokens, 1, [&](const int64_t begin, const int64_t end) {
 for (int64_t i = begin; i < end; i++) {
 const float* token_data = x_fp32 + i * token_dim_size;
 float min = torch::executor::vec_minf(token_data, token_dim_size);
 float max = torch::executor::vec_maxf(token_data, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(
 min, max, qmin, qmax, scale, zero_point);
 scale_data[i] = scale;
 zero_point_data[i] = zero_point;
 }
 });
 } else {
 for (auto i = 0; i < num_tokens; i++) {
 // vec_minf uses std::min_element. Check if it actually
 // gets vectorized.
 float min = torch::executor::vec_minf(x_fp32, token_dim_size);
 float max = torch::executor::vec_maxf(x_fp32, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(min, max, qmin, qmax, scale, zero_point);
 scale_out.mutable_data_ptr<double>()[i] = scale;
 zero_point_out.mutable_data_ptr<int64_t>()[i] = zero_point;
 x_fp32 += token_dim_size;
 }
 }
}
} // namespace
Expand Down
Loading
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] Add simd path for op quantize#15768

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 Merged

SS-JIA
merged 3 commits into

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Nov 14, 2025
 Merged


[Executorch] Add simd path for op quantize
#15768
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848d476

[Executorch][llm] Fix flakyness of quantized sdpa test


kimishpatel Nov 10, 2025
444ee9d

[Executorch] parallelize op_choose_qparams


kimishpatel Nov 10, 2025
414aa6f

[Executorch] Add simd path for op quantize


kimishpatel Nov 10, 2025

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1 change: 1 addition & 0 deletions
extension/llm/custom_ops/TARGETS

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -60,5 +60,6 @@ runtime.python_test(
 ],
 deps = [
 "//caffe2:torch",
 "//executorch/extension/pybindings:portable_lib",
 ],
)

6 changes: 6 additions & 0 deletions
extension/llm/custom_ops/test_quantized_sdpa.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -12,6 +12,7 @@
import torch.nn.functional as F

from executorch.extension.llm.custom_ops import custom_ops # noqa
from executorch.extension.pybindings.portable_lib import _unsafe_reset_threadpool


def is_fbcode():
Expand DownExpand Up@@ -40,6 +41,11 @@ def setUp(self):
 self.q_shape = None
 self.kv_shape = None
 self.is_seq_at_dim_2 = True
 # For some reason 4 threads doesnt work
 # This setting is needed to make this test not flaky due to OMP
 # error of "OMP: Error #131: Thread identifier invalid"
 # Not clear why that happens but having smaller threadpool resolves it
 _unsafe_reset_threadpool(3)

 def _scale_tensor(self, tensor, min_value, max_value, scale=True):
 normalized_tensor = (tensor - tensor.min()) / (tensor.max() - tensor.min())
Expand Down

48 changes: 37 additions & 11 deletions
kernels/quantized/cpu/op_choose_qparams.cpp

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,6 +8,7 @@

#include <executorch/kernels/portable/cpu/vec_ops.h>
#include <executorch/runtime/kernel/kernel_includes.h>
#include <executorch/runtime/kernel/thread_parallel_interface.h>
#include <algorithm>
#include <cinttypes>
#include <cmath>
Expand DownExpand Up@@ -202,17 +203,42 @@ void choose_qparams_per_token(
 num_tokens *= input.size(i);
 }
 auto token_dim_size = input.size(input.dim() - 1);
 for (auto i = 0; i < num_tokens; i++) {
 // vec_minf uses std::min_element. Check if it actually
 // gets vectorized.
 float min = torch::executor::vec_minf(x_fp32, token_dim_size);
 float max = torch::executor::vec_maxf(x_fp32, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(min, max, qmin, qmax, scale, zero_point);
 scale_out.mutable_data_ptr<double>()[i] = scale;
 zero_point_out.mutable_data_ptr<int64_t>()[i] = zero_point;
 x_fp32 += token_dim_size;

 const int64_t total_elements = num_tokens * token_dim_size;
 constexpr int64_t MIN_ELEMENTS_FOR_PARALLEL = 512;
 const bool use_parallel = total_elements >= MIN_ELEMENTS_FOR_PARALLEL;

 if (use_parallel) {
 auto* scale_data = scale_out.mutable_data_ptr<double>();
 auto* zero_point_data = zero_point_out.mutable_data_ptr<int64_t>();

 ::executorch::extension::parallel_for(
 0, num_tokens, 1, [&](const int64_t begin, const int64_t end) {
 for (int64_t i = begin; i < end; i++) {
 const float* token_data = x_fp32 + i * token_dim_size;
 float min = torch::executor::vec_minf(token_data, token_dim_size);
 float max = torch::executor::vec_maxf(token_data, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(
 min, max, qmin, qmax, scale, zero_point);
 scale_data[i] = scale;
 zero_point_data[i] = zero_point;
 }
 });
 } else {
 for (auto i = 0; i < num_tokens; i++) {
 // vec_minf uses std::min_element. Check if it actually
 // gets vectorized.
 float min = torch::executor::vec_minf(x_fp32, token_dim_size);
 float max = torch::executor::vec_maxf(x_fp32, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(min, max, qmin, qmax, scale, zero_point);
 scale_out.mutable_data_ptr<double>()[i] = scale;
 zero_point_out.mutable_data_ptr<int64_t>()[i] = zero_point;
 x_fp32 += token_dim_size;
 }
 }
}
} // namespace
Expand Down
Loading
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] Add simd path for op quantize#15768

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 Merged

SS-JIA
merged 3 commits into

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Nov 14, 2025
 Merged


[Executorch] Add simd path for op quantize
#15768
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848d476

[Executorch][llm] Fix flakyness of quantized sdpa test


kimishpatel Nov 10, 2025
444ee9d

[Executorch] parallelize op_choose_qparams


kimishpatel Nov 10, 2025
414aa6f

[Executorch] Add simd path for op quantize


kimishpatel Nov 10, 2025

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1 change: 1 addition & 0 deletions
extension/llm/custom_ops/TARGETS

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Original file line numberDiff line numberDiff line change
Expand Up@@ -60,5 +60,6 @@ runtime.python_test(
 ],
 deps = [
 "//caffe2:torch",
 "//executorch/extension/pybindings:portable_lib",
 ],
)

6 changes: 6 additions & 0 deletions
extension/llm/custom_ops/test_quantized_sdpa.py

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Expand Up@@ -12,6 +12,7 @@
import torch.nn.functional as F

from executorch.extension.llm.custom_ops import custom_ops # noqa
from executorch.extension.pybindings.portable_lib import _unsafe_reset_threadpool


def is_fbcode():
Expand DownExpand Up@@ -40,6 +41,11 @@ def setUp(self):
 self.q_shape = None
 self.kv_shape = None
 self.is_seq_at_dim_2 = True
 # For some reason 4 threads doesnt work
 # This setting is needed to make this test not flaky due to OMP
 # error of "OMP: Error #131: Thread identifier invalid"
 # Not clear why that happens but having smaller threadpool resolves it
 _unsafe_reset_threadpool(3)

 def _scale_tensor(self, tensor, min_value, max_value, scale=True):
 normalized_tensor = (tensor - tensor.min()) / (tensor.max() - tensor.min())
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48 changes: 37 additions & 11 deletions
kernels/quantized/cpu/op_choose_qparams.cpp

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Expand Up@@ -8,6 +8,7 @@

#include <executorch/kernels/portable/cpu/vec_ops.h>
#include <executorch/runtime/kernel/kernel_includes.h>
#include <executorch/runtime/kernel/thread_parallel_interface.h>
#include <algorithm>
#include <cinttypes>
#include <cmath>
Expand DownExpand Up@@ -202,17 +203,42 @@ void choose_qparams_per_token(
 num_tokens *= input.size(i);
 }
 auto token_dim_size = input.size(input.dim() - 1);
 for (auto i = 0; i < num_tokens; i++) {
 // vec_minf uses std::min_element. Check if it actually
 // gets vectorized.
 float min = torch::executor::vec_minf(x_fp32, token_dim_size);
 float max = torch::executor::vec_maxf(x_fp32, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(min, max, qmin, qmax, scale, zero_point);
 scale_out.mutable_data_ptr<double>()[i] = scale;
 zero_point_out.mutable_data_ptr<int64_t>()[i] = zero_point;
 x_fp32 += token_dim_size;

 const int64_t total_elements = num_tokens * token_dim_size;
 constexpr int64_t MIN_ELEMENTS_FOR_PARALLEL = 512;
 const bool use_parallel = total_elements >= MIN_ELEMENTS_FOR_PARALLEL;

 if (use_parallel) {
 auto* scale_data = scale_out.mutable_data_ptr<double>();
 auto* zero_point_data = zero_point_out.mutable_data_ptr<int64_t>();

 ::executorch::extension::parallel_for(
 0, num_tokens, 1, [&](const int64_t begin, const int64_t end) {
 for (int64_t i = begin; i < end; i++) {
 const float* token_data = x_fp32 + i * token_dim_size;
 float min = torch::executor::vec_minf(token_data, token_dim_size);
 float max = torch::executor::vec_maxf(token_data, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(
 min, max, qmin, qmax, scale, zero_point);
 scale_data[i] = scale;
 zero_point_data[i] = zero_point;
 }
 });
 } else {
 for (auto i = 0; i < num_tokens; i++) {
 // vec_minf uses std::min_element. Check if it actually
 // gets vectorized.
 float min = torch::executor::vec_minf(x_fp32, token_dim_size);
 float max = torch::executor::vec_maxf(x_fp32, token_dim_size);
 double scale;
 int32_t zero_point;
 calculate_scale_and_zero_point(min, max, qmin, qmax, scale, zero_point);
 scale_out.mutable_data_ptr<double>()[i] = scale;
 zero_point_out.mutable_data_ptr<int64_t>()[i] = zero_point;
 x_fp32 += token_dim_size;
 }
 }
}
} // namespace
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