diff --git a/.github/workflows/triton-benchmarks.yml b/.github/workflows/triton-benchmarks.yml
index a28fe59d6a..9cdf69736a 100644
--- a/.github/workflows/triton-benchmarks.yml
+++ b/.github/workflows/triton-benchmarks.yml
@@ -334,6 +334,18 @@ jobs:
           python build_report.py $REPORTS/flexAttnCausal-performance.csv $REPORTS/flexAttnCausal-batch16-triton-report.csv --benchmark flex-attn-causal-batch16 --compiler triton --param_cols "Z,H_q,H_kv,N_CTX_q,N_CTX_kv,D_HEAD_qk,D_HEAD_v" --tflops_col Triton-TFlops --hbm_col "Triton-GB/s" --tag $TAG
           python build_report.py $REPORTS/flexAttnCausal-performance.csv $REPORTS/flexAttnCausal-batch16-torch-report.csv --benchmark flex-attn-causal-batch16 --compiler torch --param_cols "Z,H_q,H_kv,N_CTX_q,N_CTX_kv,D_HEAD_qk,D_HEAD_v" --tflops_col Torch-TFlops --hbm_col "Torch-GB/s" --tag $TAG
 
+      - name: Run Triton FlexAttention Causal Mask bwd kernel benchmark
+        if: ${{ steps.install.outcome == 'success' && !cancelled() && (inputs.benchmarks == '' || contains(fromJson(inputs.benchmarks || '[]'), 'flex_attention_bwd_benchmark_causal_mask.py')) && !contains(fromJson(inputs.skip_benchmarks || '[]'), 'flex_attention_bwd_benchmark_causal_mask.py') }}
+        run: |
+          export LD_LIBRARY_PATH=$PTI_LIBS_DIR:$LD_LIBRARY_PATH
+          cd benchmarks/triton_kernels_benchmark
+          FA_KERNEL_MODE='bwd' \
+            python flex_attention_benchmark_causal_mask.py --reports $REPORTS --n_runs $N_RUNS
+
+          source ../../scripts/capture-hw-details.sh
+          python build_report.py $REPORTS/flexAttnCausal-performance.csv $REPORTS/flexAttnCausal-bwd-triton-report.csv --benchmark flex-attn-causal-bwd --compiler triton --param_cols "Z,H_q,H_kv,N_CTX_q,N_CTX_kv,D_HEAD_qk,D_HEAD_v" --tflops_col Triton-TFlops --hbm_col "Triton-GB/s" --tag $TAG
+          python build_report.py $REPORTS/flexAttnCausal-performance.csv $REPORTS/flexAttnCausal-bwd-torch-report.csv --benchmark flex-attn-causal-bwd --compiler torch --param_cols "Z,H_q,H_kv,N_CTX_q,N_CTX_kv,D_HEAD_qk,D_HEAD_v" --tflops_col Torch-TFlops --hbm_col "Torch-GB/s" --tag $TAG
+
       - name: Run Triton FlexAttention Custom Masks fwd kernel benchmark
         if: ${{ steps.install.outcome == 'success' && !cancelled() && (inputs.benchmarks == '' || contains(fromJson(inputs.benchmarks || '[]'), 'flex_attention_benchmark_custom_masks.py')) && !contains(fromJson(inputs.skip_benchmarks || '[]'), 'flex_attention_benchmark_custom_masks.py') }}
         run: |
diff --git a/benchmarks/triton_kernels_benchmark/flex_attention_benchmark_causal_mask.py b/benchmarks/triton_kernels_benchmark/flex_attention_benchmark_causal_mask.py
index e08a0135bd..3b4871d073 100644
--- a/benchmarks/triton_kernels_benchmark/flex_attention_benchmark_causal_mask.py
+++ b/benchmarks/triton_kernels_benchmark/flex_attention_benchmark_causal_mask.py
@@ -86,53 +86,58 @@ def causal_mask(_, __, q_idx, kv_idx):
 @benchmark_suite.perf_report(
     benchmark_suite.Benchmark(
         x_names=['Z', 'H_q', 'H_kv', 'N_CTX_q', 'N_CTX_kv', 'D_HEAD_qk', 'D_HEAD_v', 'MODE'],
-        x_vals=
-        # Multi-head attention. H_q equals H_kv
-        # Prefill shapes of Phi3-mini-4k-instruct
-        [[z, 32, 32, 1024, 1024, 96, 96, fa_kernel_mode] for z in batch_sizes] +
-        # Prefill shapes of Qwen3-4B
-        [[z, 32, 32, 1024, 1024, 128, 128, fa_kernel_mode] for z in batch_sizes] +
-        # Prefill shapes of DeepSeek-v3
-        [[z, 128, 128, 1024, 1024, 192, 128, fa_kernel_mode] for z in batch_sizes] +
-        # Append shapes of Phi3-mini-4k-instruct
-        [[z, 32, 32, 512, 1024 + 128 + 512, 96, 96, fa_kernel_mode] for z in batch_sizes] +
-
-        ## Multi-query attention. H_kv equals 1.
-        # Append shapes of Deepseek-v3
-        [[z, 128, 1, 512, 1024 + 128 + 512, 576, 512, fa_kernel_mode] for z in batch_sizes] +
-
-        # Grouped-query attention. H_q / H_kv > 1
-        # Prefill shapes of Llama-3.1-8B
-        [[z, 32, 8, 1024, 1024, 128, 128, fa_kernel_mode] for z in batch_sizes] +
-        # Prefill shapes of meta-llama-Llama-3.2-3B
-        [[z, 24, 8, 1024, 1024, 128, 128, fa_kernel_mode] for z in batch_sizes] +
-        # Prefill shapes of Deepseek-R1-Distill-Qwen-14B
-        [[z, 40, 8, 1024, 1024, 128, 128, fa_kernel_mode] for z in batch_sizes] +
-        # Append shapes of Llama-3.1-8B
-        [[z, 32, 8, 512, 1024 + 128 + 512, 128, 128, fa_kernel_mode] for z in batch_sizes] +
-        # Append shapes of meta-llama-Llama-3.2-3B
-        [[z, 24, 8, 512, 1024 + 128 + 512, 128, 128, fa_kernel_mode] for z in batch_sizes] +
-        # Append shapes of Qwen3-4B
-        [[z, 32, 8, 512, 1024 + 128 + 512, 128, 128, fa_kernel_mode] for z in batch_sizes] +
-
-        # FlexDecoding configuration. N_CTX_q equals 1. N_CTX_kv >= 1k
-        # Decode shapes of Llama-3.1-8B
-        [[z, 32, 8, 1, 1024 + 64, 128, 128, fa_kernel_mode] for z in batch_sizes] +
-        # Decode shapes of meta-llama-Llama-3.2-3B
-        [[z, 24, 8, 1, 1024 + 64, 128, 128, fa_kernel_mode] for z in batch_sizes] +
-        # Decode shapes of Phi3-mini-4k-instruct
-        [
-            # acc = acc.reshape(G, BLOCK_M_PER_HQ, V_HEAD_DIM)
-            # ValueError: Shape element 2 must be a power of 2
-            # [z, 32, 32, 1, 1024 + 64, 96, 96, fa_kernel_mode] for z in batch_sizes
-        ] +
-        # Decode shapes of Qwen3-4B
-        [[z, 32, 8, 1, 1024 + 64, 128, 128, fa_kernel_mode] for z in batch_sizes] +
-        # Decode shapes of Deepseek-R1-Distill-Qwen-14B
-        [[z, 40, 8, 1, 1024 + 64, 128, 128, fa_kernel_mode] for z in batch_sizes] +
-        # Decode shapes of Deepseek-v3
-        [
-            # [z, 128, 1, 1, 1024 + 64, 576, 512, fa_kernel_mode] for z in batch_sizes
+        x_vals=[
+            x_val for x_val in
+            # Multi-head attention. H_q equals H_kv
+            # Prefill shapes of Phi3-mini-4k-instruct
+            [[z, 32, 32, 1024, 1024, 96, 96, fa_kernel_mode] for z in batch_sizes] +
+            # Prefill shapes of Qwen3-4B
+            [[z, 32, 32, 1024, 1024, 128, 128, fa_kernel_mode] for z in batch_sizes] +
+            # Prefill shapes of DeepSeek-v3
+            [[z, 128, 128, 1024, 1024, 192, 128, fa_kernel_mode] for z in batch_sizes] +
+            # Append shapes of Phi3-mini-4k-instruct
+            [[z, 32, 32, 512, 1024 + 128 + 512, 96, 96, fa_kernel_mode] for z in batch_sizes] +
+
+            # Multi-query attention. H_kv equals 1.
+            # Append shapes of Deepseek-v3
+            ([[z, 128, 1, 512, 1024 + 128 + 512, 576, 512, fa_kernel_mode]
+              for z in batch_sizes] if fa_kernel_mode != 'bwd' else []) +
+
+            # Grouped-query attention. H_q / H_kv > 1
+            # Prefill shapes of Llama-3.1-8B
+            [[z, 32, 8, 1024, 1024, 128, 128, fa_kernel_mode] for z in batch_sizes] +
+            # Prefill shapes of meta-llama-Llama-3.2-3B
+            [[z, 24, 8, 1024, 1024, 128, 128, fa_kernel_mode] for z in batch_sizes] +
+            # Prefill shapes of Deepseek-R1-Distill-Qwen-14B
+            [[z, 40, 8, 1024, 1024, 128, 128, fa_kernel_mode] for z in batch_sizes] +
+            # Append shapes of Llama-3.1-8B
+            [[z, 32, 8, 512, 1024 + 128 + 512, 128, 128, fa_kernel_mode] for z in batch_sizes] +
+            # Append shapes of meta-llama-Llama-3.2-3B
+            [[z, 24, 8, 512, 1024 + 128 + 512, 128, 128, fa_kernel_mode] for z in batch_sizes] +
+            # Append shapes of Qwen3-4B
+            [[z, 32, 8, 512, 1024 + 128 + 512, 128, 128, fa_kernel_mode] for z in batch_sizes] +
+
+            # FlexDecoding configuration. N_CTX_q equals 1. N_CTX_kv >= 1k
+            # Decode shapes of Llama-3.1-8B
+            [[z, 32, 8, 1, 1024 + 64, 128, 128, fa_kernel_mode] for z in batch_sizes] +
+            # Decode shapes of meta-llama-Llama-3.2-3B
+            [[z, 24, 8, 1, 1024 + 64, 128, 128, fa_kernel_mode] for z in batch_sizes] +
+            # Decode shapes of Phi3-mini-4k-instruct
+            [
+                # acc = acc.reshape(G, BLOCK_M_PER_HQ, V_HEAD_DIM)
+                # ValueError: Shape element 2 must be a power of 2
+                # [z, 32, 32, 1, 1024 + 64, 96, 96, fa_kernel_mode] for z in batch_sizes
+            ] +
+            # Decode shapes of Qwen3-4B
+            [[z, 32, 8, 1, 1024 + 64, 128, 128, fa_kernel_mode] for z in batch_sizes] +
+            # Decode shapes of Deepseek-R1-Distill-Qwen-14B
+            [[z, 40, 8, 1, 1024 + 64, 128, 128, fa_kernel_mode] for z in batch_sizes] +
+            # Decode shapes of Deepseek-v3
+            [
+                # [z, 128, 1, 1, 1024 + 64, 576, 512, fa_kernel_mode] for z in batch_sizes
+            ]
+            # FIXME: Reenable when PyTorch fixes config in https://github.com/pytorch/pytorch/blob/main/torch/_inductor/template_heuristics/triton.py#L1509
+            if x_val[-1] == 'fwd' or x_val[5] != 128
         ],
         line_arg='provider',
         line_vals=['triton', 'torch'],