NVIDIA
diff --git a/‎qa/L1_pytorch_distributed_unittest/test.sh‎
Lines changed: 18 additions & 1 deletion b/‎qa/L1_pytorch_distributed_unittest/test.sh‎
Lines changed: 18 additions & 1 deletion
diff --git a/‎qa/L3_pytorch_FA_versions_test/test.sh‎
Lines changed: 77 additions & 8 deletions b/‎qa/L3_pytorch_FA_versions_test/test.sh‎
Lines changed: 77 additions & 8 deletions
diff --git a/‎tests/pytorch/attention/run_attention_with_cp.py‎
Lines changed: 56 additions & 40 deletions b/‎tests/pytorch/attention/run_attention_with_cp.py‎
Lines changed: 56 additions & 40 deletions
diff --git a/‎tests/pytorch/attention/test_attention.py‎
Lines changed: 14 additions & 20 deletions b/‎tests/pytorch/attention/test_attention.py‎
Lines changed: 14 additions & 20 deletions
@@ -22,14 +22,31 @@ mkdir -p "$XML_LOG_DIR"
 
 pip3 install pytest==8.2.1 || error_exit "Failed to install pytest"
 
+# Run CP tests (deterministic + non-deterministic) first so they can be parallelized.
+# Each needs 4 GPUs, so >=8 GPUs allows them to run concurrently on disjoint GPU sets.
+NUM_GPUS=$(python3 -c "import torch; print(torch.cuda.device_count())")
+echo "Detected $NUM_GPUS GPU(s)"
+if [ "$NUM_GPUS" -ge 8 ]; then
+    echo "Running CP tests in parallel: non-deterministic on GPUs 0-3, deterministic on GPUs 4-7"
+    CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m pytest -v -s --junitxml=$XML_LOG_DIR/pytest_test_attention_with_cp.xml $TE_PATH/tests/pytorch/attention/test_attention_with_cp.py &
+    PID_CP_NONDET=$!
+    CUDA_VISIBLE_DEVICES=4,5,6,7 NVTE_ALLOW_NONDETERMINISTIC_ALGO=0 python3 -m pytest -v -s --junitxml=$XML_LOG_DIR/pytest_test_attention_deterministic_with_cp.xml $TE_PATH/tests/pytorch/attention/test_attention_with_cp.py &
+    PID_CP_DET=$!
+    wait $PID_CP_NONDET || test_fail "test_attention_with_cp.py"
+    wait $PID_CP_DET || test_fail "NVTE_ALLOW_NONDETERMINISTIC_ALGO=0 test_attention_with_cp.py"
+else
+    echo "Running CP tests sequentially: need >=8 GPUs for parallel execution"
+    python3 -m pytest -v -s --junitxml=$XML_LOG_DIR/pytest_test_attention_with_cp.xml $TE_PATH/tests/pytorch/attention/test_attention_with_cp.py || test_fail "test_attention_with_cp.py"
+    NVTE_ALLOW_NONDETERMINISTIC_ALGO=0 python3 -m pytest -v -s --junitxml=$XML_LOG_DIR/pytest_test_attention_deterministic_with_cp.xml $TE_PATH/tests/pytorch/attention/test_attention_with_cp.py || test_fail "NVTE_ALLOW_NONDETERMINISTIC_ALGO=0 test_attention_with_cp.py"
+fi
+
 python3 -m pytest -v -s --junitxml=$XML_LOG_DIR/pytest_test_sanity.xml $TE_PATH/tests/pytorch/distributed/test_sanity.py || test_fail "test_sanity.py"
 python3 -m pytest -v -s --junitxml=$XML_LOG_DIR/pytest_test_numerics.xml $TE_PATH/tests/pytorch/distributed/test_numerics.py || test_fail "test_numerics.py"
 python3 -m pytest -v -s --junitxml=$XML_LOG_DIR/pytest_test_numerics_exact.xml $TE_PATH/tests/pytorch/distributed/test_numerics_exact.py || test_fail "test_numerics_exact.py"
 python3 -m pytest -v -s --junitxml=$XML_LOG_DIR/pytest_test_fusible_ops.xml $TE_PATH/tests/pytorch/distributed/test_fusible_ops.py || test_fail "test_fusible_ops.py"
 python3 -m pytest -v -s --junitxml=$XML_LOG_DIR/pytest_test_torch_fsdp2.xml $TE_PATH/tests/pytorch/distributed/test_torch_fsdp2.py || test_fail "test_torch_fsdp2.py"
 python3 -m pytest -v -s --junitxml=$XML_LOG_DIR/pytest_test_comm_gemm_overlap.xml $TE_PATH/tests/pytorch/distributed/test_comm_gemm_overlap.py || test_fail "test_comm_gemm_overlap.py"
 python3 -m pytest -v -s --junitxml=$XML_LOG_DIR/pytest_test_fusible_ops_with_userbuffers.xml $TE_PATH/tests/pytorch/distributed/test_fusible_ops_with_userbuffers.py || test_fail "test_fusible_ops_with_userbuffers.py"
-python3 -m pytest -v -s --junitxml=$XML_LOG_DIR/pytest_test_attention_with_cp.xml $TE_PATH/tests/pytorch/attention/test_attention_with_cp.py || test_fail "test_attention_with_cp.py"
 python3 -m pytest -v -s --junitxml=$XML_LOG_DIR/pytest_test_cp_utils.xml $TE_PATH/tests/pytorch/attention/test_cp_utils.py || test_fail "test_cp_utils.py"
 python3 -m pytest -v -s --junitxml=$XML_LOG_DIR/pytest_test_cast_master_weights_to_fp8.xml $TE_PATH/tests/pytorch/distributed/test_cast_master_weights_to_fp8.py || test_fail "test_cast_master_weights_to_fp8.py"
 python3 -m pytest -v -s --junitxml=$XML_LOG_DIR/pytest_test_newton_schulz.xml $TE_PATH/tests/pytorch/distributed/test_newton_schulz.py || test_fail "test_newton_schulz.py"
 
@@ -2,26 +2,44 @@
 #
 # See LICENSE for license information.
 
-set -e
+function error_exit() {
+    echo "Error: $1"
+    exit 1
+}
+
+function test_fail() {
+    RET=1
+    FAILED_CASES="$FAILED_CASES $1"
+    echo "Error: sub-test failed: $1"
+}
+
+RET=0
+FAILED_CASES=""
 
 : ${TE_PATH:=/opt/transformerengine}
 : ${XML_LOG_DIR:=/logs}
 mkdir -p "$XML_LOG_DIR"
 
-pip3 install pytest==8.2.1
+pip3 install pytest==8.2.1 || error_exit "Failed to install pytest"
 
 # Limit parallel build jobs to avoid overwhelming system resources
 export MAX_JOBS=32
 
 # Iterate over Flash Attention versions
 sm_arch=`python3 -c "import torch; sm = torch.cuda.get_device_capability(0); print(sm[0]*10+sm[1])"`
 export FLASH_ATTN_CUDA_ARCHS=$sm_arch
+# CP tests are expensive and run only once per arch:
+#   - sm90 (H100):  FA3 (3.0.0b1) - context_parallel.py only supports FA3 on Hopper
+#   - sm>90 (B200): latest FA4    - FA3 is not built/installed for sm>90
+# Non-CP tests still run for every FA version in the array.
 if [ $sm_arch -gt 90 ]
 then
-  FA_versions=(2.8.3 4.0.0b8)
+  FA_versions=(2.8.3 4.0.0b11)
+  CP_FA_VERSION="${FA_versions[-1]}"
 elif [ $sm_arch -eq 90 ]
 then
-  FA_versions=(2.7.3 2.8.3 3.0.0b1 4.0.0b8)
+  FA_versions=(2.8.3 3.0.0b1 4.0.0b11)
+  CP_FA_VERSION="3.0.0b1"
 fi
 
 for fa_version in "${FA_versions[@]}"
@@ -35,12 +53,63 @@ do
   then
     pip3 install flash-attn-4==${fa_version} nvidia-cutlass-dsl[cu13]==4.4.2 --no-build-isolation
   else
-    git clone https://github.com/Dao-AILab/flash-attention.git
-    cd flash-attention/hopper && python setup.py install
-    cd ../../
+    # FA3 source build (~20 min). Skip if FA3 is already installed.
+    if python3 -c "import flash_attn_3" 2>/dev/null; then
+      echo "FA3 already installed (from base image); skipping source build"
+    else
+      git clone https://github.com/Dao-AILab/flash-attention.git
+      cd flash-attention/hopper && python setup.py install
+      cd ../../
+    fi
   fi
 
+  # Ensure local test utils is found before nvidia-cutlass-dsl's utils package
+  export PYTHONPATH=$TE_PATH/tests/pytorch:${PYTHONPATH:-}
+
   # Run tests
-  NVTE_TORCH_COMPILE=0 python3 -m pytest -v -s --junitxml=$XML_LOG_DIR/pytest.xml $TE_PATH/tests/pytorch/attention/test_attention.py
+  NUM_GPUS=$(nvidia-smi -L | wc -l)
+  echo "Detected $NUM_GPUS GPU(s)"
+
+  # Suffix junit XMLs with the FA version so per-iteration results are preserved
+  # (otherwise pytest.xml is overwritten on each loop iteration and we lose timing
+  # data for all but the last FA version).
+  fa_tag="${fa_version//./_}"
+  XML_ATTN="$XML_LOG_DIR/pytest_test_attention_fa${fa_tag}.xml"
+  XML_CP="$XML_LOG_DIR/pytest_test_attention_with_cp_fa${fa_tag}.xml"
+
+  if [ "$fa_version" = "$CP_FA_VERSION" ]; then
+    echo "Running CP tests with FA $fa_version (CP version for sm$sm_arch)"
+    if [ "$NUM_GPUS" -ge 5 ]; then
+      CP_NUM_GPUS=$(( NUM_GPUS - 1 > 4 ? 4 : NUM_GPUS - 1 ))
+      CP_GPUS=$(seq -s, 1 $CP_NUM_GPUS)
+      echo "Running tests in parallel: test_attention.py on GPU 0, test_attention_with_cp.py on GPUs $CP_GPUS ($CP_NUM_GPUS GPUs)"
+
+      CUDA_VISIBLE_DEVICES=0 NVTE_TORCH_COMPILE=0 python3 -m pytest -v -s \
+        --junitxml=$XML_ATTN \
+        $TE_PATH/tests/pytorch/attention/test_attention.py &
+      PID_ATTN=$!
 
+      CUDA_VISIBLE_DEVICES=$CP_GPUS NVTE_TORCH_COMPILE=0 python3 -m pytest -v -s \
+        --junitxml=$XML_CP \
+        $TE_PATH/tests/pytorch/attention/test_attention_with_cp.py &
+      PID_CP=$!
+
+      wait $PID_ATTN || test_fail "test_attention.py (FA $fa_version)"
+      wait $PID_CP || test_fail "test_attention_with_cp.py (FA $fa_version)"
+    else
+      echo "Running tests sequentially: need >=5 GPUs for parallel execution (1 for test_attention + 4 for test_attention_with_cp)"
+      NVTE_TORCH_COMPILE=0 python3 -m pytest -v -s --junitxml=$XML_ATTN $TE_PATH/tests/pytorch/attention/test_attention.py || test_fail "test_attention.py (FA $fa_version)"
+      NVTE_TORCH_COMPILE=0 python3 -m pytest -v -s --junitxml=$XML_CP $TE_PATH/tests/pytorch/attention/test_attention_with_cp.py || test_fail "test_attention_with_cp.py (FA $fa_version)"
+    fi
+  else
+    echo "Skipping CP tests for FA $fa_version (CP only runs with FA $CP_FA_VERSION on sm$sm_arch)"
+    NVTE_TORCH_COMPILE=0 python3 -m pytest -v -s --junitxml=$XML_ATTN $TE_PATH/tests/pytorch/attention/test_attention.py || test_fail "test_attention.py (FA $fa_version)"
+  fi
 done
+
+if [ "$RET" -ne 0 ]; then
+    echo "Error in the following test cases:$FAILED_CASES"
+    exit 1
+fi
+echo "All tests passed"
+exit 0
@@ -47,6 +47,7 @@ def generate_input_shapes(
     config: ModelConfig,
     world_size: int,
     kernel_backend: str,
+    fa_pad_between_seqs: str = "False",
 ):
     if qkv_format == "bshd":
         q_input_shape = (
@@ -115,9 +116,12 @@ def generate_input_shapes(
         ).cuda()
         cu_seqlens_q = torch.clone(cu_seqlens_q_padded)
 
-        # Since FlashAttention doesn't support pad b/w sequences, and FusedAttention does,
-        # cu_seqlens_q is updated to reflect non-padded lengths for FusedAttention only.
-        if kernel_backend == "FusedAttention":
+        # Generate padded data (cu_seqlens_q reflects non-padded lengths, so it
+        # differs from cu_seqlens_q_padded) for FusedAttention always, and for
+        # FlashAttention only when its test param requests it. DPA auto-detects
+        # pad_between_seqs downstream from the cu_seqlens_q vs cu_seqlens_q_padded
+        # mismatch.
+        if kernel_backend == "FusedAttention" or fa_pad_between_seqs == "True":
             cu_seqlens_q[1:] = seqlens_q.cumsum(0, dtype=torch.int32).cuda()
 
         # NOTE: In case of Cross-Attention, `cu_seqlens_kv` and `cu_seqlens_kv_padded`
@@ -196,6 +200,7 @@ def run_dpa_with_cp(
     scaling_mode="delayed",
     f16_O="False",
     is_training="True",
+    fa_pad_between_seqs="False",
     deterministic="False",
     log_level=logging.WARNING,
 ):
@@ -314,7 +319,7 @@ def run_dpa_with_cp(
         cu_seqlens_kv,
         cu_seqlens_q_padded,
         cu_seqlens_kv_padded,
-    ) = generate_input_shapes(qkv_format, config, world_size, kernel_backend)
+    ) = generate_input_shapes(qkv_format, config, world_size, kernel_backend, fa_pad_between_seqs)
     q_orig = torch.clamp(torch.randn(q_input_shape, dtype=dtypes[dtype]), min=-1, max=1).cuda()
     k_orig = torch.clamp(torch.randn(k_input_shape, dtype=dtypes[dtype]), min=-1, max=1).cuda()
     v_orig = torch.clamp(torch.randn(v_input_shape, dtype=dtypes[dtype]), min=-1, max=1).cuda()
@@ -557,11 +562,11 @@ def run_dpa_with_cp(
                 tensors_to_deq[i] = tensor.dequantize()
         if not fp8_bwd:
             tensors[0], tensors[5] = tensors_to_deq
-    for i, tensor in enumerate(tensors):
+    for tensor, name in zip(tensors, names):
         # dbias/dbias_ could be None, so skip check for it
         if tensor is not None:
-            assert torch.all(~torch.isnan(tensor)), f"{names[i]} contains NaN"
-            assert torch.all(~torch.isinf(tensor)), f"{names[i]} contains Inf"
+            assert torch.all(~torch.isnan(tensor)), f"{name} has nan values"
+            assert torch.all(~torch.isinf(tensor)), f"{name} has inf values"
     out, dq, dk, dv, dbias, out_, dq_, dk_, dv_, dbias_ = tensors
 
     ############  compare results between CP and no-CP ############
@@ -617,49 +622,60 @@ def run_dpa_with_cp(
         if is_training:
             dq, out = [x.index_select(0, seq_idx_q).contiguous() for x in [dq, out]]
             dk, dv = [x.index_select(0, seq_idx_kv).contiguous() for x in [dk, dv]]
-            dq_, dk_, dv_, out_ = [dq_, dk_, dv_, out_]
             cu_seqlens_q_padded = cu_seqlens_q_padded // world_size
             cu_seqlens_q = get_cu_seqlens_on_cp_rank(
                 cu_seqlens_q, cu_seqlens_q_padded, world_size, rank, True, True
             )
-            cu_pads_q = cu_seqlens_q_padded - cu_seqlens_q
-            num_pads_q = cu_pads_q[1:] - cu_pads_q[:-1]
-            for x in [dq, out, dq_, out_]:
-                assert torch.count_nonzero(x[cu_seqlens_q_padded[-1] :]).item() == 0
-                for b in range(config.batch_size):
-                    assert (
-                        num_pads_q[b] == 0
-                        or torch.count_nonzero(
-                            x[
-                                (cu_seqlens_q_padded[b + 1] - num_pads_q[b]) : cu_seqlens_q_padded[
-                                    b + 1
-                                ]
-                            ]
-                        ).item()
-                        == 0
-                    )
+            num_pads_q = (cu_seqlens_q_padded - cu_seqlens_q)[1:] - (
+                cu_seqlens_q_padded - cu_seqlens_q
+            )[:-1]
             cu_seqlens_kv_padded = cu_seqlens_kv_padded // world_size
             cu_seqlens_kv = get_cu_seqlens_on_cp_rank(
                 cu_seqlens_kv, cu_seqlens_kv_padded, world_size, rank, True, True
             )
-            cu_pads_kv = cu_seqlens_kv_padded - cu_seqlens_kv
-            num_pads_kv = cu_pads_kv[1:] - cu_pads_kv[:-1]
-            for x in [dk, dv, dk_, dv_]:
-                assert torch.count_nonzero(x[cu_seqlens_kv_padded[-1] :]).item() == 0
-                for b in range(config.batch_size):
-                    assert (
-                        num_pads_kv[b] == 0
-                        or torch.count_nonzero(
-                            x[
-                                (
-                                    cu_seqlens_kv_padded[b + 1] - num_pads_kv[b]
-                                ) : cu_seqlens_kv_padded[b + 1]
-                            ]
-                        ).item()
-                        == 0
+            num_pads_kv = (cu_seqlens_kv_padded - cu_seqlens_kv)[1:] - (
+                cu_seqlens_kv_padded - cu_seqlens_kv
+            )[:-1]
+            # FA3 leaves garbage at padding positions despite seqused_q/k (tile spillover).
+            # Forward out_ can't be pre-zeroed because FA3's custom op returns out_ as an
+            # output rather than mutating it in-place, triggering PyTorch's aliasing constraint.
+            # Backward dq/dk/dv CAN be pre-zeroed because FA3 marks them as mutated inputs.
+            if fa_pad_between_seqs == "True":
+                # out_ is a view inside the CP custom autograd Function, so in-place
+                # zeroing is blocked by PyTorch. Clone to break the view relationship.
+                out_ = out_.clone()
+                for x in [out, out_, dq]:
+                    for b in range(config.batch_size):
+                        x[
+                            cu_seqlens_q_padded[b + 1] - num_pads_q[b] : cu_seqlens_q_padded[b + 1]
+                        ] = 0.0
+                    x[cu_seqlens_q_padded[-1] :] = 0.0
+                for x in [dk, dv]:
+                    for b in range(config.batch_size):
+                        x[
+                            cu_seqlens_kv_padded[b + 1]
+                            - num_pads_kv[b] : cu_seqlens_kv_padded[b + 1]
+                        ] = 0.0
+                    x[cu_seqlens_kv_padded[-1] :] = 0.0
+                # Verify CP backward tensors have clean padding (pre-zeroed in context_parallel.py).
+                for xname, x, cu, np_ in [
+                    ("dq_", dq_, cu_seqlens_q_padded, num_pads_q),
+                    ("dk_", dk_, cu_seqlens_kv_padded, num_pads_kv),
+                    ("dv_", dv_, cu_seqlens_kv_padded, num_pads_kv),
+                ]:
+                    nnz = torch.count_nonzero(x[cu[-1] :]).item()
+                    assert nnz == 0, (
+                        f"{xname} has {nnz} nonzero values in tail padding — "
+                        "context_parallel.py should zero padding positions"
                     )
+                    for b in range(config.batch_size):
+                        if np_[b] > 0:
+                            nnz = torch.count_nonzero(x[cu[b + 1] - np_[b] : cu[b + 1]]).item()
+                            assert nnz == 0, (
+                                f"{xname} has {nnz} nonzero values in batch {b} padding — "
+                                "context_parallel.py should zero padding positions"
+                            )
         else:
-            # Forward-only: reshape only out/out_ for comparison
             out = out.index_select(0, seq_idx_q).contiguous()
             out_ = out_
 
 
@@ -124,7 +124,7 @@ def reset_global_fp8_state():
 @pytest.mark.parametrize("workspace_opt", [True, False])
 @pytest.mark.parametrize("qkv_layout", [None])
 @pytest.mark.parametrize("swa", [False])
-@pytest.mark.parametrize("pad_between_seqs", [False])
+@pytest.mark.parametrize("pad_between_seqs", [False, True])
 def test_dot_product_attention(
     dtype,
     model_configs,
@@ -157,6 +157,8 @@ def test_dot_product_attention(
 
     config.window_size = check_set_window_size(config.attn_mask_type, config.window_size)
     qkv_format = qkv_layout.replace("3", "").replace("2", "").split("_")[0]
+    if pad_between_seqs and qkv_format != "thd":
+        pytest.skip("pad_between_seqs only applies to THD format!")
     if qkv_format == "thd" and "padding" not in config.attn_mask_type:
         config.attn_mask_type = (
             "padding_" + config.attn_mask_type if config.attn_mask_type != "no_mask" else "padding"
@@ -195,19 +197,6 @@ def test_dot_product_attention(
         )
         flash_attn_supported, fused_attn_supported, unfused_attn_supported = available_backends
 
-    # FlashAttention does not support pad_between_seqs, but _run_dot_product_attention
-    # mannually pads and unpads the input and output of FlashAttention for testing purposes
-    if (
-        pad_between_seqs
-        and FlashAttentionUtils.is_installed
-        and not (
-            config.max_seqlen_q != config.max_seqlen_kv
-            and config.attn_mask_type in ["causal", "padding_causal"]
-        )
-        and (config.window_size[0] == -1 or FlashAttentionUtils.v2_3_plus)
-    ):
-        flash_attn_supported = True
-
     # Skip if only unfused backend is supported
     if (len(fused_attn_backends) + flash_attn_supported + unfused_attn_supported) < 2:
         pytest.skip("Less than two backends to compare.")
@@ -1301,12 +1290,12 @@ def get_dummy_cuda_rng_tracker() -> CudaRNGStatesTracker:
         block.softmax_offset.requires_grad = True
 
     # Run a forward and backward pass
-    if backend in ["FlashAttention", "UnfusedDotProductAttention"]:
+    if backend in ["UnfusedDotProductAttention"]:
         q = inp_orig[0]
         k = inp_orig[1]
         v = inp_orig[2]
         d_out = out_grad_orig
-    if backend == "FusedAttention":
+    if backend in ["FusedAttention", "FlashAttention"]:
         q = inp[0]
         k = inp[1]
         v = inp[2]
@@ -1322,14 +1311,19 @@ def get_dummy_cuda_rng_tracker() -> CudaRNGStatesTracker:
         max_seqlen_kv=config.max_seqlen_kv,
         cu_seqlens_q=cu_seqlens_q,
         cu_seqlens_kv=cu_seqlens_kv,
-        cu_seqlens_q_padded=cu_seqlens_q_after_pad if backend == "FusedAttention" else None,
-        cu_seqlens_kv_padded=cu_seqlens_kv_after_pad if backend == "FusedAttention" else None,
+        cu_seqlens_q_padded=(
+            cu_seqlens_q_after_pad if backend in ["FusedAttention", "FlashAttention"] else None
+        ),
+        cu_seqlens_kv_padded=(
+            cu_seqlens_kv_after_pad if backend in ["FusedAttention", "FlashAttention"] else None
+        ),
         attn_mask_type=config.attn_mask_type,
         checkpoint_core_attention=ckpt_attn,
         core_attention_bias_type=config.attn_bias_type,
         core_attention_bias=bias,
         alibi_slopes=alibi_slopes,
         fast_zero_fill=True,
+        pad_between_seqs=pad_between_seqs,
         # Only pass num_splits when exercising the FlashAttention path
         num_splits=config.num_splits if backend == "FlashAttention" else 1,
     )
@@ -1343,12 +1337,12 @@ def get_dummy_cuda_rng_tracker() -> CudaRNGStatesTracker:
     if is_training and config.softmax_type != "vanilla":
         d_softmax_offset = block.softmax_offset.grad
 
-    if backend in ["FlashAttention", "UnfusedDotProductAttention"]:
+    if backend in ["UnfusedDotProductAttention"]:
         if is_training:
             return out, max_logit, (q.grad, k.grad, v.grad, d_softmax_offset)
         else:
             return out, max_logit, (None, None, None, d_softmax_offset)
-    if backend == "FusedAttention":
+    if backend in ["FusedAttention", "FlashAttention"]:
         if qkv_format == "thd" and pad_between_seqs:
             out_orig = torch.Tensor([]).to(device="cuda", dtype=dtype)
             if is_training: