From 85b89ecbe211cfe86f76edf5f077010d842a77b4 Mon Sep 17 00:00:00 2001
From: Brian Hirsh <hirsheybar@meta.com>
Date: Mon, 27 Feb 2023 19:32:50 +0000
Subject: [PATCH] fix embedding_backward_dense decomp with broadcasting
 (#95499)

Fixes https://github.com/pytorch/pytorch/issues/95182

Pull Request resolved: https://github.com/pytorch/pytorch/pull/95499
Approved by: https://github.com/ezyang, https://github.com/ngimel
---
 test/dynamo/test_repros.py      | 19 +++++++++++++++++++
 torch/_decomp/decompositions.py |  2 +-
 2 files changed, 20 insertions(+), 1 deletion(-)

diff --git a/test/dynamo/test_repros.py b/test/dynamo/test_repros.py
index b0226025fb0..4e34b2cd142 100644
--- a/test/dynamo/test_repros.py
+++ b/test/dynamo/test_repros.py
@@ -857,6 +857,25 @@ def f(x):
 
         f(torch.ones(2, device="cuda", dtype=torch.float64))
 
+    def test_embedding_backward_broadcasting_decomp(self):
+        def f(grad_output, indices):
+            num_weights = 10
+            padding_idx = 1
+            scale_grad_by_freq = True
+            return torch.ops.aten.embedding_dense_backward(
+                grad_output, indices, num_weights, padding_idx, scale_grad_by_freq
+            )
+
+        f_compiled = torch.compile(f, backend="aot_eager")
+
+        grad_output = torch.ones(2, 4, 3, dtype=torch.float16)
+        indices = torch.ones(2, 4, dtype=torch.int64)
+
+        out_ref = f(grad_output, indices)
+        out_test = f_compiled(grad_output, indices)
+
+        self.assertEqual(out_ref, out_test)
+
     def test_reformer_eval(self):
         with torch.no_grad():
             cnt = self._reformer(nopython=True)
diff --git a/torch/_decomp/decompositions.py b/torch/_decomp/decompositions.py
index d2964f2bbd2..54266e1bd37 100644
--- a/torch/_decomp/decompositions.py
+++ b/torch/_decomp/decompositions.py
@@ -1071,7 +1071,7 @@ def embedding_dense_backward(
         ones = torch.ones_like(indices)
         counts = counts.index_put([indices], ones, accumulate=True)
         grad_weights_scale = counts[indices]
-        grad_output = grad_output / grad_weights_scale.unsqueeze(1)
+        grad_output = grad_output / grad_weights_scale.unsqueeze(-1)
 
     mask = _unsqueeze_to_dim(indices == padding_idx, grad_output.ndim)
     grad = grad_output.masked_fill(mask, 0)