[JAX] Fix failing fused attn tests for dropout=0.1 and bias for sm100 (#2135)

* Fix failing tests for dropout=0.1 and bias for fused attn for blackwell
Signed-off-by: Kshitij Lakhani <klakhani@nvidia.com>

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* Fix the skip message
Signed-off-by: Kshitij Lakhani <klakhani@nvidia.com>

* Assert in fused attn bwd pass for sm100
Signed-off-by: Kshitij Lakhani <klakhani@nvidia.com>

Add check for sm100
Signed-off-by: Kshitij Lakhani <klakhani@nvidia.com>

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* Add support to get all devs in the process for jax
Signed-off-by: Kshitij Lakhani <klakhani@nvidia.com>

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* Code clean up
Signed-off-by: Kshitij Lakhani <klakhani@nvidia.com>

* Make get_all_device_compute_capability more pythonic, thereby avoiding unnecessary type conversion
Signed-off-by: Kshitij Lakhani <klakhani@nvidia.com>

* Represent attn bias using enum instead of string
Signed-off-by: Kshitij Lakhani <klakhani@nvidia.com>

---------
Signed-off-by: Kshitij Lakhani <klakhani@nvidia.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

tests/jax/test_fused_attn.py

@@ -41,6 +41,7 @@ from transformer_engine.jax.cpp_extensions import FusedAttnHelper
 from transformer_engine_jax import (
     NVTE_Fused_Attn_Backend,
     get_cudnn_version,
+    get_device_compute_capability,
 )
 
 from distributed_test_base import assert_equal_collectives
@@ -348,6 +349,14 @@ class FusedAttnRunner:
                 "seqlen_q > seqlen_kv is not supported with sliding window attention in cuDNN"
             )
 
+        if (
+            get_device_compute_capability(0) == 100
+            and self.dropout_prob == 0.1
+            and self.attn_bias_type is not AttnBiasType.NO_BIAS
+        ):
+            pytest.skip(
+                "For sm100, bprop kernel support for dropout + determinism (bias) is not supported"
+            )
         # Test the MLA case where head dims for qk differ from head dims for v, only if the tensors
         # are provided in BSHD_BSHD_BSHD or THD_THD_THD formats
         if self.head_dim_qk != self.head_dim_v and not self.qkv_layout.is_separate():

transformer_engine/jax/cpp_extensions/attention.py

@@ -34,6 +34,7 @@ from .misc import (
     te_dtype_to_jax_dtype,
     get_padded_spec,
     get_cudnn_version,
+    get_all_device_compute_capability,
 )
 from ..sharding import (
     global_mesh_resource,
@@ -2745,6 +2746,11 @@ def fused_attn_bwd(
         assert bias is None
         bias = jnp.zeros(0, dtype=qkv[0].dtype)
 
+    if 100 in get_all_device_compute_capability():
+        assert not (
+            attn_bias_type != AttnBiasType.NO_BIAS and dropout_probability != 0
+        ), "For sm100, bprop kernel support for dropout + determinism (bias) is not supported"
+
     fused_config = _FusedAttnConfig(
         attn_bias_type=attn_bias_type,
         attn_mask_type=attn_mask_type,

transformer_engine/jax/cpp_extensions/misc.py

@@ -193,6 +193,16 @@ def get_min_device_compute_capability():
     )
 
 
+def get_all_device_compute_capability():
+    """
+    Returns a list of compute capability of all local devices.
+    """
+    return tuple(
+        transformer_engine_jax.get_device_compute_capability(local_gpu_id)
+        for local_gpu_id in range(len(jax.local_devices()))
+    )
+
+
 def should_apply_1x_fused_dbias_war_for_arch_l_100(is_dbias: bool = False, quantizer=None):
     """
     Fused dbias is not supported for arch < 100 for 1x quantization, so we need to apply a workaround to