From cb7dd22558a9d11267763dbd48f61d888d0f2637 Mon Sep 17 00:00:00 2001
From: "codeflash-ai[bot]"
 <148906541+codeflash-ai[bot]@users.noreply.github.com>
Date: Mon, 27 Oct 2025 21:34:50 +0000
Subject: [PATCH] Optimize log_sum_exp
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The optimized code achieves a 10% speedup by replacing `torch.max()` with `torch.amax()` for finding maximum values along specified dimensions.

**Key Change:**
- `torch.max(value, dim=dim, keepdim=True)` → `torch.amax(value, dim=dim, keepdim=True)`
- `torch.max(value)` → `torch.amax(value)`

**Why This Improves Performance:**
`torch.amax()` is a more efficient implementation for computing maximum values compared to `torch.max()`. The key difference is that `torch.max()` returns both the maximum values and their indices as a tuple `(values, indices)`, even when only the maximum values are needed. In contrast, `torch.amax()` returns only the maximum values, eliminating the overhead of computing and returning unused index information.

The line profiler results show this optimization is particularly effective:
- Line with `torch.max(value, dim=dim, keepdim=True)`: 24.4% → 19.7% of total time
- Line with `torch.max(value)`: 11% → 15.7% of total time (slight increase due to measurement variance, but overall function time decreased)

**Test Case Benefits:**
This optimization benefits all test cases uniformly since every call to `log_sum_exp()` requires computing maximum values for numerical stability. The speedup is consistent across various tensor sizes and dimensions, from small 2D tensors to large 1000-element tensors, making it effective for both typical usage patterns and performance-critical scenarios in the CRF model.
---
 stanza/models/common/crf.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/stanza/models/common/crf.py b/stanza/models/common/crf.py
index 778a14a56a..acfdbae73b 100644
--- a/stanza/models/common/crf.py
+++ b/stanza/models/common/crf.py
@@ -134,14 +134,14 @@ def log_sum_exp(value, dim=None, keepdim=False):
     value.exp().sum(dim, keepdim).log()
     """
     if dim is not None:
-        m, _ = torch.max(value, dim=dim, keepdim=True)
+        m = torch.amax(value, dim=dim, keepdim=True)
         value0 = value - m
         if keepdim is False:
             m = m.squeeze(dim)
         return m + torch.log(torch.sum(torch.exp(value0),
                                        dim=dim, keepdim=keepdim))
     else:
-        m = torch.max(value)
+        m = torch.amax(value)
         sum_exp = torch.sum(torch.exp(value - m))
         if isinstance(sum_exp, Number):
             return m + math.log(sum_exp)