Skip to content

Fix flash_attn inferface when seq_q is not equal seq_k#400

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
benkerd22 wants to merge 1 commit into from
Closed

Fix flash_attn inferface when seq_q is not equal seq_k #400

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
34 changes: 24 additions & 10 deletions transformer_engine/pytorch/attention.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -508,35 +508,49 @@ def forward(
query_layer, key_layer, value_layer = [x.transpose(0,1).contiguous()
for x in (query_layer, key_layer, value_layer)]

batch_size, seqlen = query_layer.shape[0], query_layer.shape[1]
batch_size, seqlen_q = query_layer.shape[0], query_layer.shape[1]
seqlen_k = key_layer.shape[1]

# [b, sq, np, hn]
query_layer, key_layer, value_layer = [
x.view(x.shape[0] * x.shape[1], *x.shape[2:])
for x in [query_layer, key_layer, value_layer]
]

max_seqlen = seqlen
cu_seqlens = torch.arange(
cu_seqlens_q = torch.arange(
0,
(batch_size + 1) * seqlen,
step=seqlen,
(batch_size + 1) * seqlen_q,
step=seqlen_q,
dtype=torch.int32,
device=query_layer.device)

if self.training:
# during training q,k,v always have same seqlen
assert seqlen_k == seqlen_q

is_causal = self.attn_causal_mask
cu_seqlens_k = cu_seqlens_q
dropout_p = self.attention_dropout
else:
# turn off FA causal mask after first inference autoregressive iteration
# only on first autoregressive step q,k,v have same seqlen
is_causal = seqlen_q == seqlen_k
cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32,
device=query_layer.device)
dropout_p = 0

with self.attention_dropout_ctx():
fa_optional_forward_kwargs = {}
if not _flash_attn_2_available:
fa_optional_forward_kwargs["deterministic"] = self.deterministic
output = flash_attn_forward_func(
query_layer, key_layer, value_layer, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen,
self.attention_dropout if self.training else 0.0,
softmax_scale=1.0/self.norm_factor, causal=self.attn_causal_mask,
query_layer, key_layer, value_layer, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
dropout_p,
softmax_scale=1.0/self.norm_factor, causal=is_causal,
**fa_optional_forward_kwargs
)

# [(b sq), np, hn] -> [sq, b, (np hn)]
return output.view(batch_size, seqlen, -1).transpose(0, 1).contiguous()
return output.view(batch_size, seqlen_q, -1).transpose(0, 1).contiguous()


class FusedAttnFunc_qkvpacked(torch.autograd.Function):
Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
 blocks
(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
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;';
btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
btn.onclick = function() {
navigator.clipboard.writeText(codeBlock.textContent).then(function() {
btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
};
codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
});
}
addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
Fix flash_attn inferface when seq_q is not equal seq_k by benkerd22 · Pull Request #400 · NVIDIA/TransformerEngine · GitHub
Skip to content
Fix flash_attn inferface when seq_q is not equal seq_k#400

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Closed

benkerd22
wants to merge
1
commit into

from

 Closed


Fix flash_attn inferface when seq_q is not equal seq_k
#400
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


34 changes: 24 additions & 10 deletions
transformer_engine/pytorch/attention.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -508,35 +508,49 @@ def forward(
 query_layer, key_layer, value_layer = [x.transpose(0,1).contiguous()
 for x in (query_layer, key_layer, value_layer)]

 batch_size, seqlen = query_layer.shape[0], query_layer.shape[1]
 batch_size, seqlen_q = query_layer.shape[0], query_layer.shape[1]
 seqlen_k = key_layer.shape[1]

 # [b, sq, np, hn]
 query_layer, key_layer, value_layer = [
 x.view(x.shape[0] * x.shape[1], *x.shape[2:])
 for x in [query_layer, key_layer, value_layer]
 ]

 max_seqlen = seqlen
 cu_seqlens = torch.arange(
 cu_seqlens_q = torch.arange(
 0,
 (batch_size + 1) * seqlen,
 step=seqlen,
 (batch_size + 1) * seqlen_q,
 step=seqlen_q,
 dtype=torch.int32,
 device=query_layer.device)

 if self.training:
 # during training q,k,v always have same seqlen
 assert seqlen_k == seqlen_q

 is_causal = self.attn_causal_mask
 cu_seqlens_k = cu_seqlens_q
 dropout_p = self.attention_dropout
 else:
 # turn off FA causal mask after first inference autoregressive iteration
 # only on first autoregressive step q,k,v have same seqlen
 is_causal = seqlen_q == seqlen_k
 cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32,
 device=query_layer.device)
 dropout_p = 0

 with self.attention_dropout_ctx():
 fa_optional_forward_kwargs = {}
 if not _flash_attn_2_available:
 fa_optional_forward_kwargs["deterministic"] = self.deterministic
 output = flash_attn_forward_func(
 query_layer, key_layer, value_layer, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen,
self.attention_dropout if self.training else 0.0,
 softmax_scale=1.0/self.norm_factor, causal=self.attn_causal_mask,
 query_layer, key_layer, value_layer, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
dropout_p,
 softmax_scale=1.0/self.norm_factor, causal=is_causal,
 **fa_optional_forward_kwargs
 )

 # [(b sq), np, hn] -> [sq, b, (np hn)]
 return output.view(batch_size, seqlen, -1).transpose(0, 1).contiguous()
 return output.view(batch_size, seqlen_q, -1).transpose(0, 1).contiguous()


class FusedAttnFunc_qkvpacked(torch.autograd.Function):
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
.markdown-body table th, .markdown-body table td { border-color: #30363d !important; }
.markdown-body img { background: #0d1117; }
.markdown-body blockquote { border-left-color: #8b949e; }
.markdown-body hr { border-color: #30363d; }
';
document.head.appendChild(style);
})();
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Fix flash_attn inferface when seq_q is not equal seq_k by benkerd22 · Pull Request #400 · NVIDIA/TransformerEngine · GitHub
Skip to content
Fix flash_attn inferface when seq_q is not equal seq_k#400

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Closed

benkerd22
wants to merge
1
commit into

from

 Closed


Fix flash_attn inferface when seq_q is not equal seq_k
#400
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


34 changes: 24 additions & 10 deletions
transformer_engine/pytorch/attention.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -508,35 +508,49 @@ def forward(
 query_layer, key_layer, value_layer = [x.transpose(0,1).contiguous()
 for x in (query_layer, key_layer, value_layer)]

 batch_size, seqlen = query_layer.shape[0], query_layer.shape[1]
 batch_size, seqlen_q = query_layer.shape[0], query_layer.shape[1]
 seqlen_k = key_layer.shape[1]

 # [b, sq, np, hn]
 query_layer, key_layer, value_layer = [
 x.view(x.shape[0] * x.shape[1], *x.shape[2:])
 for x in [query_layer, key_layer, value_layer]
 ]

 max_seqlen = seqlen
 cu_seqlens = torch.arange(
 cu_seqlens_q = torch.arange(
 0,
 (batch_size + 1) * seqlen,
 step=seqlen,
 (batch_size + 1) * seqlen_q,
 step=seqlen_q,
 dtype=torch.int32,
 device=query_layer.device)

 if self.training:
 # during training q,k,v always have same seqlen
 assert seqlen_k == seqlen_q

 is_causal = self.attn_causal_mask
 cu_seqlens_k = cu_seqlens_q
 dropout_p = self.attention_dropout
 else:
 # turn off FA causal mask after first inference autoregressive iteration
 # only on first autoregressive step q,k,v have same seqlen
 is_causal = seqlen_q == seqlen_k
 cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32,
 device=query_layer.device)
 dropout_p = 0

 with self.attention_dropout_ctx():
 fa_optional_forward_kwargs = {}
 if not _flash_attn_2_available:
 fa_optional_forward_kwargs["deterministic"] = self.deterministic
 output = flash_attn_forward_func(
 query_layer, key_layer, value_layer, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen,
self.attention_dropout if self.training else 0.0,
 softmax_scale=1.0/self.norm_factor, causal=self.attn_causal_mask,
 query_layer, key_layer, value_layer, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
dropout_p,
 softmax_scale=1.0/self.norm_factor, causal=is_causal,
 **fa_optional_forward_kwargs
 )

 # [(b sq), np, hn] -> [sq, b, (np hn)]
 return output.view(batch_size, seqlen, -1).transpose(0, 1).contiguous()
 return output.view(batch_size, seqlen_q, -1).transpose(0, 1).contiguous()


class FusedAttnFunc_qkvpacked(torch.autograd.Function):
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
}
}
if (terms.length === 0) return;
var style = document.createElement('style');
style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
if (node.nodeType === 3) { // text node
var text = node.textContent;
var found = false;
terms.forEach(function(term) {
var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi');
if (regex.test(text)) {
found = true;
var frag = document.createDocumentFragment();
var parts = text.split(regex);
parts.forEach(function(part, i) {
if (i % 2 === 0) {
frag.appendChild(document.createTextNode(part));
} else {
var span = document.createElement('span');
span.className = 'userscript-highlight';
span.textContent = part;
frag.appendChild(span);
}
});
node.parentNode.replaceChild(frag, node);
}
});
} else if (node.nodeType === 1 && node.childNodes) { // element
var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];
if (!skipTags.includes(node.tagName)) {
Array.from(node.childNodes).forEach(highlight);
}
}
}
highlight(document.body);
// Re-highlight on dynamic content
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Fix flash_attn inferface when seq_q is not equal seq_k by benkerd22 · Pull Request #400 · NVIDIA/TransformerEngine · GitHub
Skip to content
Fix flash_attn inferface when seq_q is not equal seq_k#400

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Closed

benkerd22
wants to merge
1
commit into

from

 Closed


Fix flash_attn inferface when seq_q is not equal seq_k
#400
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


34 changes: 24 additions & 10 deletions
transformer_engine/pytorch/attention.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -508,35 +508,49 @@ def forward(
 query_layer, key_layer, value_layer = [x.transpose(0,1).contiguous()
 for x in (query_layer, key_layer, value_layer)]

 batch_size, seqlen = query_layer.shape[0], query_layer.shape[1]
 batch_size, seqlen_q = query_layer.shape[0], query_layer.shape[1]
 seqlen_k = key_layer.shape[1]

 # [b, sq, np, hn]
 query_layer, key_layer, value_layer = [
 x.view(x.shape[0] * x.shape[1], *x.shape[2:])
 for x in [query_layer, key_layer, value_layer]
 ]

 max_seqlen = seqlen
 cu_seqlens = torch.arange(
 cu_seqlens_q = torch.arange(
 0,
 (batch_size + 1) * seqlen,
 step=seqlen,
 (batch_size + 1) * seqlen_q,
 step=seqlen_q,
 dtype=torch.int32,
 device=query_layer.device)

 if self.training:
 # during training q,k,v always have same seqlen
 assert seqlen_k == seqlen_q

 is_causal = self.attn_causal_mask
 cu_seqlens_k = cu_seqlens_q
 dropout_p = self.attention_dropout
 else:
 # turn off FA causal mask after first inference autoregressive iteration
 # only on first autoregressive step q,k,v have same seqlen
 is_causal = seqlen_q == seqlen_k
 cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32,
 device=query_layer.device)
 dropout_p = 0

 with self.attention_dropout_ctx():
 fa_optional_forward_kwargs = {}
 if not _flash_attn_2_available:
 fa_optional_forward_kwargs["deterministic"] = self.deterministic
 output = flash_attn_forward_func(
 query_layer, key_layer, value_layer, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen,
self.attention_dropout if self.training else 0.0,
 softmax_scale=1.0/self.norm_factor, causal=self.attn_causal_mask,
 query_layer, key_layer, value_layer, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
dropout_p,
 softmax_scale=1.0/self.norm_factor, causal=is_causal,
 **fa_optional_forward_kwargs
 )

 # [(b sq), np, hn] -> [sq, b, (np hn)]
 return output.view(batch_size, seqlen, -1).transpose(0, 1).contiguous()
 return output.view(batch_size, seqlen_q, -1).transpose(0, 1).contiguous()


class FusedAttnFunc_qkvpacked(torch.autograd.Function):
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
Fix flash_attn inferface when seq_q is not equal seq_k by benkerd22 · Pull Request #400 · NVIDIA/TransformerEngine · GitHub
Skip to content
Fix flash_attn inferface when seq_q is not equal seq_k#400

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Closed

benkerd22
wants to merge
1
commit into

from

 Closed


Fix flash_attn inferface when seq_q is not equal seq_k
#400
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


34 changes: 24 additions & 10 deletions
transformer_engine/pytorch/attention.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -508,35 +508,49 @@ def forward(
 query_layer, key_layer, value_layer = [x.transpose(0,1).contiguous()
 for x in (query_layer, key_layer, value_layer)]

 batch_size, seqlen = query_layer.shape[0], query_layer.shape[1]
 batch_size, seqlen_q = query_layer.shape[0], query_layer.shape[1]
 seqlen_k = key_layer.shape[1]

 # [b, sq, np, hn]
 query_layer, key_layer, value_layer = [
 x.view(x.shape[0] * x.shape[1], *x.shape[2:])
 for x in [query_layer, key_layer, value_layer]
 ]

 max_seqlen = seqlen
 cu_seqlens = torch.arange(
 cu_seqlens_q = torch.arange(
 0,
 (batch_size + 1) * seqlen,
 step=seqlen,
 (batch_size + 1) * seqlen_q,
 step=seqlen_q,
 dtype=torch.int32,
 device=query_layer.device)

 if self.training:
 # during training q,k,v always have same seqlen
 assert seqlen_k == seqlen_q

 is_causal = self.attn_causal_mask
 cu_seqlens_k = cu_seqlens_q
 dropout_p = self.attention_dropout
 else:
 # turn off FA causal mask after first inference autoregressive iteration
 # only on first autoregressive step q,k,v have same seqlen
 is_causal = seqlen_q == seqlen_k
 cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32,
 device=query_layer.device)
 dropout_p = 0

 with self.attention_dropout_ctx():
 fa_optional_forward_kwargs = {}
 if not _flash_attn_2_available:
 fa_optional_forward_kwargs["deterministic"] = self.deterministic
 output = flash_attn_forward_func(
 query_layer, key_layer, value_layer, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen,
self.attention_dropout if self.training else 0.0,
 softmax_scale=1.0/self.norm_factor, causal=self.attn_causal_mask,
 query_layer, key_layer, value_layer, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
dropout_p,
 softmax_scale=1.0/self.norm_factor, causal=is_causal,
 **fa_optional_forward_kwargs
 )

 # [(b sq), np, hn] -> [sq, b, (np hn)]
 return output.view(batch_size, seqlen, -1).transpose(0, 1).contiguous()
 return output.view(batch_size, seqlen_q, -1).transpose(0, 1).contiguous()


class FusedAttnFunc_qkvpacked(torch.autograd.Function):
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Fix flash_attn inferface when seq_q is not equal seq_k by benkerd22 · Pull Request #400 · NVIDIA/TransformerEngine · GitHub
Skip to content
Fix flash_attn inferface when seq_q is not equal seq_k#400

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Closed

benkerd22
wants to merge
1
commit into

from

 Closed


Fix flash_attn inferface when seq_q is not equal seq_k
#400
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


34 changes: 24 additions & 10 deletions
transformer_engine/pytorch/attention.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -508,35 +508,49 @@ def forward(
 query_layer, key_layer, value_layer = [x.transpose(0,1).contiguous()
 for x in (query_layer, key_layer, value_layer)]

 batch_size, seqlen = query_layer.shape[0], query_layer.shape[1]
 batch_size, seqlen_q = query_layer.shape[0], query_layer.shape[1]
 seqlen_k = key_layer.shape[1]

 # [b, sq, np, hn]
 query_layer, key_layer, value_layer = [
 x.view(x.shape[0] * x.shape[1], *x.shape[2:])
 for x in [query_layer, key_layer, value_layer]
 ]

 max_seqlen = seqlen
 cu_seqlens = torch.arange(
 cu_seqlens_q = torch.arange(
 0,
 (batch_size + 1) * seqlen,
 step=seqlen,
 (batch_size + 1) * seqlen_q,
 step=seqlen_q,
 dtype=torch.int32,
 device=query_layer.device)

 if self.training:
 # during training q,k,v always have same seqlen
 assert seqlen_k == seqlen_q

 is_causal = self.attn_causal_mask
 cu_seqlens_k = cu_seqlens_q
 dropout_p = self.attention_dropout
 else:
 # turn off FA causal mask after first inference autoregressive iteration
 # only on first autoregressive step q,k,v have same seqlen
 is_causal = seqlen_q == seqlen_k
 cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32,
 device=query_layer.device)
 dropout_p = 0

 with self.attention_dropout_ctx():
 fa_optional_forward_kwargs = {}
 if not _flash_attn_2_available:
 fa_optional_forward_kwargs["deterministic"] = self.deterministic
 output = flash_attn_forward_func(
 query_layer, key_layer, value_layer, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen,
self.attention_dropout if self.training else 0.0,
 softmax_scale=1.0/self.norm_factor, causal=self.attn_causal_mask,
 query_layer, key_layer, value_layer, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
dropout_p,
 softmax_scale=1.0/self.norm_factor, causal=is_causal,
 **fa_optional_forward_kwargs
 )

 # [(b sq), np, hn] -> [sq, b, (np hn)]
 return output.view(batch_size, seqlen, -1).transpose(0, 1).contiguous()
 return output.view(batch_size, seqlen_q, -1).transpose(0, 1).contiguous()


class FusedAttnFunc_qkvpacked(torch.autograd.Function):
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Fix flash_attn inferface when seq_q is not equal seq_k by benkerd22 · Pull Request #400 · NVIDIA/TransformerEngine · GitHub
Skip to content
Fix flash_attn inferface when seq_q is not equal seq_k#400

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Closed

benkerd22
wants to merge
1
commit into

from

 Closed


Fix flash_attn inferface when seq_q is not equal seq_k
#400
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


34 changes: 24 additions & 10 deletions
transformer_engine/pytorch/attention.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -508,35 +508,49 @@ def forward(
 query_layer, key_layer, value_layer = [x.transpose(0,1).contiguous()
 for x in (query_layer, key_layer, value_layer)]

 batch_size, seqlen = query_layer.shape[0], query_layer.shape[1]
 batch_size, seqlen_q = query_layer.shape[0], query_layer.shape[1]
 seqlen_k = key_layer.shape[1]

 # [b, sq, np, hn]
 query_layer, key_layer, value_layer = [
 x.view(x.shape[0] * x.shape[1], *x.shape[2:])
 for x in [query_layer, key_layer, value_layer]
 ]

 max_seqlen = seqlen
 cu_seqlens = torch.arange(
 cu_seqlens_q = torch.arange(
 0,
 (batch_size + 1) * seqlen,
 step=seqlen,
 (batch_size + 1) * seqlen_q,
 step=seqlen_q,
 dtype=torch.int32,
 device=query_layer.device)

 if self.training:
 # during training q,k,v always have same seqlen
 assert seqlen_k == seqlen_q

 is_causal = self.attn_causal_mask
 cu_seqlens_k = cu_seqlens_q
 dropout_p = self.attention_dropout
 else:
 # turn off FA causal mask after first inference autoregressive iteration
 # only on first autoregressive step q,k,v have same seqlen
 is_causal = seqlen_q == seqlen_k
 cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32,
 device=query_layer.device)
 dropout_p = 0

 with self.attention_dropout_ctx():
 fa_optional_forward_kwargs = {}
 if not _flash_attn_2_available:
 fa_optional_forward_kwargs["deterministic"] = self.deterministic
 output = flash_attn_forward_func(
 query_layer, key_layer, value_layer, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen,
self.attention_dropout if self.training else 0.0,
 softmax_scale=1.0/self.norm_factor, causal=self.attn_causal_mask,
 query_layer, key_layer, value_layer, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
dropout_p,
 softmax_scale=1.0/self.norm_factor, causal=is_causal,
 **fa_optional_forward_kwargs
 )

 # [(b sq), np, hn] -> [sq, b, (np hn)]
 return output.view(batch_size, seqlen, -1).transpose(0, 1).contiguous()
 return output.view(batch_size, seqlen_q, -1).transpose(0, 1).contiguous()


class FusedAttnFunc_qkvpacked(torch.autograd.Function):
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
Fix flash_attn inferface when seq_q is not equal seq_k by benkerd22 · Pull Request #400 · NVIDIA/TransformerEngine · GitHub
Skip to content
Fix flash_attn inferface when seq_q is not equal seq_k#400

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Closed

benkerd22
wants to merge
1
commit into

from

 Closed


Fix flash_attn inferface when seq_q is not equal seq_k
#400
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


34 changes: 24 additions & 10 deletions
transformer_engine/pytorch/attention.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -508,35 +508,49 @@ def forward(
 query_layer, key_layer, value_layer = [x.transpose(0,1).contiguous()
 for x in (query_layer, key_layer, value_layer)]

 batch_size, seqlen = query_layer.shape[0], query_layer.shape[1]
 batch_size, seqlen_q = query_layer.shape[0], query_layer.shape[1]
 seqlen_k = key_layer.shape[1]

 # [b, sq, np, hn]
 query_layer, key_layer, value_layer = [
 x.view(x.shape[0] * x.shape[1], *x.shape[2:])
 for x in [query_layer, key_layer, value_layer]
 ]

 max_seqlen = seqlen
 cu_seqlens = torch.arange(
 cu_seqlens_q = torch.arange(
 0,
 (batch_size + 1) * seqlen,
 step=seqlen,
 (batch_size + 1) * seqlen_q,
 step=seqlen_q,
 dtype=torch.int32,
 device=query_layer.device)

 if self.training:
 # during training q,k,v always have same seqlen
 assert seqlen_k == seqlen_q

 is_causal = self.attn_causal_mask
 cu_seqlens_k = cu_seqlens_q
 dropout_p = self.attention_dropout
 else:
 # turn off FA causal mask after first inference autoregressive iteration
 # only on first autoregressive step q,k,v have same seqlen
 is_causal = seqlen_q == seqlen_k
 cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32,
 device=query_layer.device)
 dropout_p = 0

 with self.attention_dropout_ctx():
 fa_optional_forward_kwargs = {}
 if not _flash_attn_2_available:
 fa_optional_forward_kwargs["deterministic"] = self.deterministic
 output = flash_attn_forward_func(
 query_layer, key_layer, value_layer, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen,
self.attention_dropout if self.training else 0.0,
 softmax_scale=1.0/self.norm_factor, causal=self.attn_causal_mask,
 query_layer, key_layer, value_layer, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
dropout_p,
 softmax_scale=1.0/self.norm_factor, causal=is_causal,
 **fa_optional_forward_kwargs
 )

 # [(b sq), np, hn] -> [sq, b, (np hn)]
 return output.view(batch_size, seqlen, -1).transpose(0, 1).contiguous()
 return output.view(batch_size, seqlen_q, -1).transpose(0, 1).contiguous()


class FusedAttnFunc_qkvpacked(torch.autograd.Function):
Expand Down