merge nv_release_v2.10 to release_v2.10

Signed-off-by: wenjh <wenjh@sugon.com>

tests/pytorch/distributed/test_comm_gemm_overlap.py

@@ -127,12 +127,18 @@ def _run_layer_with_overlap(
     os.environ["PYTORCH_JIT"] = "0"
     os.environ["NVTE_TORCH_COMPILE"] = "0"
     os.environ["NVTE_ALLOW_NONDETERMINISTIC_ALGO"] = "0"
+    if te.get_device_compute_capability() <= (8, 0):
+        # We've experienced numerical discrepancies in Flash Attention
+        # backward when running with Userbuffers on A100s. This does
+        # not show up in more recent GPUs.
+        os.environ["NVTE_FLASH_ATTN"] = "0"
 
     result = subprocess.run(test_cmd, env=os.environ, capture_output=True, check=False)
 
     os.unsetenv("PYTORCH_JIT")
     os.unsetenv("NVTE_TORCH_COMPILE")
     os.unsetenv("NVTE_ALLOW_NONDETERMINISTIC_ALGO")
+    os.unsetenv("NVTE_FLASH_ATTN")
 
     if (
         result.returncode != 0

tests/pytorch/distributed/test_sanity.py

@@ -7,7 +7,7 @@ import sys
 import pytest
 import torch
 import transformer_engine
-from transformer_engine.pytorch import DotProductAttention, TransformerLayer, Linear
+from transformer_engine.pytorch import DotProductAttention, TransformerLayer, Linear, GroupedLinear
 
 _current_file = pathlib.Path(__file__).resolve()
 sys.path.append(str(_current_file.parent.parent))
@@ -19,7 +19,9 @@ model_configs = {
 
 
 @pytest.mark.parametrize("model", ["small"])
-@pytest.mark.parametrize("module", ["TransformerLayer", "DotProductAttention", "Linear"])
+@pytest.mark.parametrize(
+    "module", ["TransformerLayer", "DotProductAttention", "Linear", "GroupedLinear"]
+)
 def test_current_device(model, module):
     """Test cases where current device is different from tensor device"""
 
@@ -42,7 +44,29 @@ def test_current_device(model, module):
             self_attn_mask_type="padding",
             device=f"cuda:{tensor_device}",
         )
-        num_tokens = torch.randint(0, config.max_seqlen_q, (1,)).item()
+        seqlens_q = torch.randint(
+            1,
+            config.max_seqlen_q,
+            [config.batch_size],
+            dtype=torch.int32,
+            device=f"cuda:{tensor_device}",
+        )
+        cu_seqlens_q = torch.zeros(
+            config.batch_size + 1, dtype=torch.int32, device=f"cuda:{tensor_device}"
+        )
+        cu_seqlens_q[1:] = torch.cumsum(seqlens_q, dim=0)
+        seqlens_kv = torch.randint(
+            1,
+            config.max_seqlen_kv,
+            [config.batch_size],
+            dtype=torch.int32,
+            device=f"cuda:{tensor_device}",
+        )
+        cu_seqlens_kv = torch.zeros(
+            config.batch_size + 1, dtype=torch.int32, device=f"cuda:{tensor_device}"
+        )
+        cu_seqlens_kv[1:] = torch.cumsum(seqlens_kv, dim=0)
+        num_tokens = cu_seqlens_q[-1]
         args = [
             torch.randn(
                 (num_tokens, config.hidden_size),
@@ -51,37 +75,55 @@ def test_current_device(model, module):
                 requires_grad=True,
             )
         ]
-        cu_seqlens_q, cu_seqlens_kv = [
-            torch.Tensor([0, 2, 3]).to(dtype=torch.int32, device=tensor_device) for _ in range(2)
-        ]
         kwargs["cu_seqlens_q"] = cu_seqlens_q
         kwargs["cu_seqlens_kv"] = cu_seqlens_kv
         kwargs["max_seqlen_q"] = config.max_seqlen_q
         kwargs["max_seqlen_kv"] = config.max_seqlen_kv
-    if module == "DotProductAttention":
+    elif module == "DotProductAttention":
         model = DotProductAttention(
             config.num_heads, config.head_dim_qk, qkv_format="thd", attn_mask_type="padding"
         )
-        num_tokens = torch.randint(0, config.max_seqlen_q, (1,)).item()
+        seqlens_q = torch.randint(
+            1,
+            config.max_seqlen_q,
+            [config.batch_size],
+            dtype=torch.int32,
+            device=f"cuda:{tensor_device}",
+        )
+        cu_seqlens_q = torch.zeros(
+            config.batch_size + 1, dtype=torch.int32, device=f"cuda:{tensor_device}"
+        )
+        cu_seqlens_q[1:] = torch.cumsum(seqlens_q, dim=0)
+        seqlens_kv = torch.randint(
+            1,
+            config.max_seqlen_kv,
+            [config.batch_size],
+            dtype=torch.int32,
+            device=f"cuda:{tensor_device}",
+        )
+        cu_seqlens_kv = torch.zeros(
+            config.batch_size + 1, dtype=torch.int32, device=f"cuda:{tensor_device}"
+        )
+        cu_seqlens_kv[1:] = torch.cumsum(seqlens_kv, dim=0)
+        num_tokens = cu_seqlens_q[-1]
         args = [
             torch.randn(
                 num_tokens,
                 config.num_heads,
                 config.head_dim_qk,
                 dtype=dtype,
-                device=tensor_device,
+                device=f"cuda:{tensor_device}",
                 requires_grad=True,
             )
             for _ in range(3)
         ]
-        cu_seqlens_q, cu_seqlens_kv = [
-            torch.Tensor([0, 2, 3]).to(dtype=torch.int32, device=tensor_device) for _ in range(2)
-        ]
         kwargs["cu_seqlens_q"] = cu_seqlens_q
         kwargs["cu_seqlens_kv"] = cu_seqlens_kv
         kwargs["max_seqlen_q"] = config.max_seqlen_q
         kwargs["max_seqlen_kv"] = config.max_seqlen_kv
-        bwd_args = [torch.randn(num_tokens, config.hidden_size, dtype=dtype, device=tensor_device)]
+        bwd_args = [
+            torch.randn(num_tokens, config.hidden_size, dtype=dtype, device=f"cuda:{tensor_device}")
+        ]
     elif module == "Linear":
         model = Linear(
             config.hidden_size,
@@ -97,6 +139,24 @@ def test_current_device(model, module):
                 requires_grad=True,
             )
         ]
+    elif module == "GroupedLinear":
+        num_gemms = 4
+        model = GroupedLinear(
+            num_gemms,
+            config.hidden_size,
+            4 * config.hidden_size,
+            params_dtype=dtype,
+            device=f"cuda:{tensor_device}",
+        )
+        args = [
+            torch.randn(
+                (config.max_seqlen_q * config.batch_size * (num_gemms - 1), config.hidden_size),
+                dtype=dtype,
+                device=f"cuda:{tensor_device}",
+                requires_grad=True,
+            ),
+            [0] + [config.max_seqlen_q * config.batch_size] * (num_gemms - 1),  # Empty first split.
+        ]
 
     current_device_before = torch.cuda.current_device()
     out = model(*args, **kwargs)

tests/pytorch/test_fusible_ops.py

@@ -913,15 +913,15 @@ class TestBasicOps:
                 dtype=dtype,
                 accumulate_into_main_grad=accumulate_into_main_grad,
             )
+            forward = te_ops.Sequential(
+                te_ops.Quantize(forward=quantized_input, backward=quantized_grad_input),
+                op,
+                te_ops.Quantize(forward=quantized_output, backward=quantized_grad_output),
+            )
         with torch.no_grad():
             op.weight.copy_(w_test)
             del w_test
             op.weight.main_grad = torch.full_like(op.weight, 0.5, dtype=torch.float32)
-        forward = te_ops.Sequential(
-            te_ops.Quantize(forward=quantized_input, backward=quantized_grad_input),
-            op,
-            te_ops.Quantize(forward=quantized_output, backward=quantized_grad_output),
-        )
         with te.autocast(enabled=quantized_compute, recipe=recipe):
             y_test = forward(x_test)
         y_test.backward(dy_test)

tests/pytorch/test_numerics.py

@@ -46,7 +46,6 @@ from transformer_engine.pytorch import (
 from transformer_engine.pytorch import torch_version
 from transformer_engine.pytorch import checkpoint as te_checkpoint
 from transformer_engine.pytorch.cpp_extensions import general_gemm, general_grouped_gemm
-from transformer_engine.pytorch.module.base import get_multi_stream_cublas_workspace, get_workspace
 from transformer_engine.common import recipe
 import transformer_engine_torch as tex
 from utils import ModelConfig, reset_rng_states
@@ -2757,7 +2756,6 @@ def test_grouped_gemm(shape, dtype, layout, accumulate, use_cutlass):
         general_gemm(
             A[i],
             B[i],
-            get_workspace(),
             dtype,
             grad=grad,
             accumulate=accumulate,
@@ -2772,7 +2770,6 @@ def test_grouped_gemm(shape, dtype, layout, accumulate, use_cutlass):
         B,
         out,
         dtype,
-        get_multi_stream_cublas_workspace(),
         m_splits=m_splits,
         grad=grad,
         accumulate=accumulate,
@@ -2832,7 +2829,6 @@ def test_fp8gemm_with_unfused_quantization(N, datatype, input_quantizer, out_qua
     quantized_out, *_ = general_gemm(
         weight_fp8,
         inp_fp8,
-        get_workspace(),
         outp_type,
         quantization_params=out_quantizer,
         bias=None,
@@ -2842,7 +2838,6 @@ def test_fp8gemm_with_unfused_quantization(N, datatype, input_quantizer, out_qua
     out, *_ = general_gemm(
         weight_fp8,
         inp_fp8,
-        get_workspace(),
         outp_type,
         quantization_params=None,
         bias=None,
@@ -2918,7 +2913,6 @@ def test_fp8_grouped_gemm(shape, accumulate):
         general_gemm(
             A_fp8[i],
             B_fp8[i],
-            get_workspace(),
             dtype,
             out=out_ref[i],
             accumulate=accumulate,
@@ -2928,7 +2922,6 @@ def test_fp8_grouped_gemm(shape, accumulate):
         B_fp8,
         out,
         dtype,
-        get_multi_stream_cublas_workspace(),
         m_splits=m_splits,
         accumulate=accumulate,
     )

tests/pytorch/test_sanity.py

@@ -37,7 +37,6 @@ from transformer_engine.pytorch import (
 from transformer_engine.common import recipe
 import transformer_engine_torch as tex
 from transformer_engine.pytorch.cpp_extensions import general_gemm
-from transformer_engine.pytorch.module.base import get_workspace
 from transformer_engine.pytorch.tensor.utils import replace_raw_data
 from utils import ModelConfig
 
@@ -961,7 +960,7 @@ def test_sanity_gemm_with_unalignment(N, offset, datatype):
     inp = torch.reshape(scratchpad[offset:-offset], (N, N))
     weight = torch.reshape(scratchpad[offset * 2 :], (N, N))
 
-    _ = general_gemm(A=weight, B=inp, workspace=get_workspace())
+    _ = general_gemm(A=weight, B=inp)
     torch.cuda.synchronize()
 
 
@@ -985,7 +984,6 @@ def test_sanity_fp8_gemm_with_unalignment(N, datatype):
     general_gemm(
         weight_fp8,
         inp_fp8,
-        get_workspace(),
         outp_type,
         bias=None,
         use_split_accumulator=False,

tests/pytorch/utils.py

@@ -8,6 +8,7 @@ import logging
 import os
 from contextlib import contextmanager
 from typing import Optional, Tuple, Dict, Any, List
+from packaging.version import Version as PkgVersion
 
 import torch
 
@@ -210,6 +211,7 @@ class ModelConfig:
         max_ctx_len: int = None,
         num_layers: int = 1,
         eps: float = 1e-5,
+        num_splits=1,
     ):
         self.batch_size = batch_size
         self.max_seqlen_q = max_seqlen_q
@@ -239,6 +241,7 @@ class ModelConfig:
         self.max_ctx_len = max_ctx_len
         self.num_layers = num_layers
         self.eps = eps
+        self.num_splits = num_splits
 
 
 @contextmanager
@@ -321,6 +324,9 @@ def get_available_attention_backends(
             inference_params=inference_params,
             softmax_type=config.softmax_type,
             return_max_logit=config.return_max_logit,
+            # allow all backends to pass so they can be used for testing;
+            # check for FA3 availability later
+            num_splits=1,
         )
         (
             use_flash_attention,
@@ -330,6 +336,10 @@ def get_available_attention_backends(
             use_unfused_attention,
             available_backends,
         ) = get_attention_backend(attention_params)
+        # Check if FA3 is an available backend when num_splits != 1
+        if available_backends[0]:
+            if config.num_splits != 1 and not flash_attention_backend > PkgVersion("3.0.0b"):
+                available_backends[0] = False
         # Set attention.py _attention_backends var using return value
         # from get_attention_backend()
         _attention_backends["use_flash_attention"] = use_flash_attention

transformer_engine/common/fused_attn/kv_cache.cu

@@ -278,7 +278,7 @@ void convert_bshd_to_thd(Tensor tensor, Tensor cu_seqlens, Tensor new_tensor, in
 /***************************************************************************************************
  * KV Cache: Copy new KV tokens to the KV cache
  *   1. new_k and new_v are in qkv_format; k_cache and v_cache are in 'bshd' format
- *   2. cu_new_lens and cu_cached_lens are in shape [b + 1]; cu_cached_lens include the added lens
+ *   2. cu_new_lens and cu_cached_lens are of shape [b + 1]; cu_cached_lens include the added lens
  *      in current step
  *   3. Non-paged KV cache is a special case of paged KV cache, with page_table = [b, 1] and
  *      max_pages_per_seq = 1. We use the same underlying kernel for both non-paged and paged.

transformer_engine/common/include/transformer_engine/fused_attn.h

@@ -131,7 +131,7 @@ enum NVTE_Mask_Type {
  *  NVTE_VANILLA_SOFTMAX: S[:,:,:,i] = exp(S[:,:,:,i])/sum(exp(S[:,:,:,:]), dim=-1),
  *  NVTE_OFF_BY_ONE_SOFTMAX: S[:,:,:,i] = exp(S[:,:,:,i])/(1 + sum(exp(S[:,:,:,:]), dim=-1)), and
  *  NVTE_LEARNABLE_SOFTMAX: S[:,j,:,i] = exp(S[:,j,:,i])/(exp(alpha[j]) + sum(exp(S[:,j,:,:]), dim=-1)),
- *  where alpha is a learnable parameter in shape [H].
+ *  where alpha is a learnable parameter of shape [H].
  */
 enum NVTE_Softmax_Type {
   /*! Vanilla softmax */

transformer_engine/common/recipe/__init__.py

@@ -50,7 +50,7 @@ class MMParams:
 
     Parameters
     ----------
-    use_split_accumulator : bool, default = `True`
+    use_split_accumulator : bool, default = True
         Use FP8 fast accumulation on Hopper or Ada. For more details,
         see CUBLASLT_MATMUL_DESC_FAST_ACCUM option for cublasLtMatmul.
     """
@@ -159,7 +159,7 @@ class DelayedScaling(Recipe):
                                                               recipe: DelayedScaling) -> Tensor
 
                                  where `Tensor` is a framework tensor type.
-    reduce_amax: bool, default = `True`
+    reduce_amax: bool, default = True
                 By default, if `torch.distributed` is initialized, the `amax` value for FP8
                 tensors is reduced across the `amax_reduction_group` (specified in the `autocast`
                 call). This keeps the amaxes and scaling factors synced across the given
@@ -167,13 +167,13 @@ class DelayedScaling(Recipe):
                 GPU maintains local amaxes and scaling factors. To ensure results are
                 numerically identical across checkpointing boundaries in this case, all
                 ranks must checkpoint in order to store the local tensors.
-    fp8_dpa: bool, default = `False`
+    fp8_dpa: bool, default = False
              Whether to enable FP8 dot product attention (DPA). When the model is placed in an
              `autocast(enabled=True)` region and `fp8_dpa` is set to `True`, DPA casts the
              inputs from higher precision to FP8, performs attention in FP8, and casts tensors
              back to higher precision as outputs. FP8 DPA currently is only supported in the
              `FusedAttention` backend.
-    fp8_mha: bool, default = `False`
+    fp8_mha: bool, default = False
             Whether to enable FP8 multi-head attention (MHA). When `True`, it removes the casting
             operations mentioned above at the DPA boundaries. Currently only standard MHA modules
             i.e. `LayerNormLinear/Linear + DPA + Linear`, are supported for this feature. When
@@ -422,11 +422,11 @@ class NVFP4BlockScaling(Recipe):
     ----------
     fp4_format : {Format.E2M1}, default = Format.E2M1
              FP4 data type.
-    disable_rht : bool, default = `False`
+    disable_rht : bool, default = False
              If set to `True`, random Hadamard transforms are not applied to any tensor.
-    disable_stochastic_rounding : bool, default = `False`
+    disable_stochastic_rounding : bool, default = False
              If set to `True`, stochastic rounding is disabled during quantization for all tensors.
-    disable_2d_quantization : bool, default = `False`
+    disable_2d_quantization : bool, default = False
              If set to `True`, 1D block scaling with block size 16 is used for all tensors.
     """
 
@@ -492,17 +492,19 @@ class CustomRecipe(Recipe):
     Parameters
     ----------
     qfactory : Callable
-               Factory callable that returns a quantizer instance for a
-               given semantic tensor role.
-               The callable is typically invoked as:
-                   qfactory(
-                       role: str,
-                   )
-
-               Where `role` is one of the following strings for e.g. te.Linear
-               (stable public contract):
-               - forward:  "linear_input", "linear_weight", "linear_output"
-               - backward: "linear_grad_output", "linear_grad_input"
+        Factory callable that returns a quantizer instance for a
+        given semantic tensor role.
+        The callable is typically invoked as::
+
+            qfactory(
+                role: str,
+            )
+
+        Where `role` is one of the following strings for e.g. te.Linear
+        (stable public contract):
+
+        - forward:  "linear_input", "linear_weight", "linear_output"
+        - backward: "linear_grad_output", "linear_grad_input"
     """
 
     qfactory: Callable[..., Any]

transformer_engine/common/transformer_engine.cpp

@@ -736,12 +736,17 @@ int nvte_is_non_tn_fp8_gemm_supported() {
 #if USE_ROCM
   return true;
 #else
-  int deviceComputeCapability =
-      transformer_engine::cuda::sm_arch(transformer_engine::cuda::current_device());
-
-  // Note: this is temporary restriction and should be lifted in the future.
-  // (remove the note once it's done.)
-  return (deviceComputeCapability >= 100 && deviceComputeCapability < 120) ||
-         deviceComputeCapability >= 130;
+  int num_devices = transformer_engine::cuda::num_devices();
+  static std::vector<int> cache(num_devices, -1);
+  static std::vector<std::once_flag> flags(num_devices);
+  int device_id = transformer_engine::cuda::current_device();
+  std::call_once(flags[device_id], [&]() {
+    int deviceComputeCapability = transformer_engine::cuda::sm_arch(device_id);
+    // Note: this is temporary restriction and should be lifted in the future.
+    // (remove the note once it's done.)
+    cache[device_id] = (deviceComputeCapability >= 100 && deviceComputeCapability < 120) ||
+                       deviceComputeCapability >= 130;
+  });
+  return cache[device_id];
 #endif
 }

transformer_engine/jax/attention.py

@@ -18,6 +18,7 @@ from transformer_engine_jax import NVTE_Mask_Type
 from transformer_engine_jax import NVTE_QKV_Layout
 from transformer_engine_jax import NVTE_QKV_Format
 from transformer_engine_jax import nvte_get_qkv_format
+from transformer_engine_jax import NVTE_Softmax_Type
 
 from . import cpp_extensions as tex
 
@@ -74,6 +75,35 @@ class AttnMaskType(Enum):
         ]
 
 
+class AttnSoftmaxType(Enum):
+    """
+    VANILLA_SOFTMAX: S[:,:,:,i] = exp(S[:,:,:,i])/sum(exp(S[:,:,:,:]), dim=-1),
+    OFF_BY_ONE_SOFTMAX: S[:,:,:,i] = exp(S[:,:,:,i])/(1 + sum(exp(S[:,:,:,:]), dim=-1)),
+    LEARNABLE_SOFTMAX: S[:,j,:,i] = exp(S[:,j,:,i])/(exp(alpha[j]) + sum(exp(S[:,j,:,:]), dim=-1)),
+    where alpha is a learnable parameter in shape [H].
+    """
+
+    VANILLA_SOFTMAX = NVTE_Softmax_Type.NVTE_VANILLA_SOFTMAX
+    OFF_BY_ONE_SOFTMAX = NVTE_Softmax_Type.NVTE_OFF_BY_ONE_SOFTMAX
+    LEARNABLE_SOFTMAX = NVTE_Softmax_Type.NVTE_LEARNABLE_SOFTMAX
+
+    @classmethod
+    def from_str(cls, softmax_type: str) -> "AttnSoftmaxType":
+        """Convert string to AttnSoftmaxType: 'vanilla', 'off_by_one', or 'learnable'."""
+        softmax_type_map = {
+            "vanilla": cls.VANILLA_SOFTMAX,
+            "off_by_one": cls.OFF_BY_ONE_SOFTMAX,
+            "learnable": cls.LEARNABLE_SOFTMAX,
+        }
+        result = softmax_type_map.get(softmax_type)
+        if result is None:
+            raise ValueError(
+                f"Unknown softmax_type: {softmax_type}. "
+                "Valid options: 'vanilla', 'off_by_one', 'learnable'"
+            )
+        return result
+
+
 class QKVFormat(Enum):
     """
     SBHD: q,k,v memory layout with [s, b, ..., h, d]
@@ -301,6 +331,7 @@ def is_fused_attn_kernel_available(
     qkv_layout,
     attn_bias_type,
     attn_mask_type,
+    softmax_type,
     dropout_probability,
     q_num_heads,
     kv_num_heads,
@@ -313,6 +344,7 @@ def is_fused_attn_kernel_available(
     """
     To check whether the fused attention kernel is supported
     """
+    window_size_tuple = (-1, -1) if window_size is None else window_size
 
     def make_helper(attn_mask_type):
         return tex.FusedAttnHelper(
@@ -322,6 +354,7 @@ def is_fused_attn_kernel_available(
             qkv_layout,
             attn_bias_type,
             attn_mask_type,
+            softmax_type,
             dropout_probability,
             q_num_heads,
             kv_num_heads,
@@ -329,7 +362,7 @@ def is_fused_attn_kernel_available(
             kv_max_seqlen,
             head_dim_qk,
             head_dim_v,
-            (-1, -1) if window_size is None else window_size,
+            window_size_tuple,
         )
 
     return make_helper(attn_mask_type).is_fused_attn_kernel_available()
@@ -497,6 +530,11 @@ def _segment_ids_pos_to_seqlens_offsets(
     #
     # This fast path avoids expanding the mask to Q * KV matrix and instead allows us to
     # examine only O(Q+KV) elements.
+
+    # For seqlens and seqoffsets calculations, the intermediate(temp) attn_mask creation
+    # using the segment ids and pos along with mask type (causal or brcm) is sufficient.
+    # It does not need to involve SW for this mask's creation
+
     # TODO(KshitijLakhani): Try exercising the fast path for BRCM as well
     if (attn_mask_type.is_causal() and window_size is None) or (
         window_size == (-1, -1) and not attn_mask_type.is_bottom_right()
@@ -558,21 +596,6 @@ def _segment_ids_pos_to_seqlens_offsets(
         )
         attn_mask = jnp.logical_and(segment_mask, causal_mask)
 
-    # TODO(KshitijLakhani): Evaluate if swa_mask is needed to procure seqlen and offsets
-    swa_mask = (
-        make_swa_mask(
-            segment_pos_q,
-            segment_pos_kv,
-            window_size,
-            dtype=jnp.bool,
-            segment_ids_q=segment_ids_q,
-            segment_ids_kv=segment_ids_kv,
-        )
-        if attn_mask_type.is_bottom_right()
-        else make_swa_mask(segment_pos_q, segment_pos_kv, window_size, dtype=jnp.bool)
-    )
-    attn_mask = jnp.logical_and(attn_mask, swa_mask)
-
     attn_mask_with_id = jnp.where(attn_mask, segment_mask_with_id, 0)
     q_seqlen, q_offset, kv_seqlen, kv_offset = _mask_to_seqlens_offset(
         attn_mask_with_id, max_segments_per_seq
@@ -786,6 +809,7 @@ def _legacy_fused_attn(
     attn_bias_type: AttnBiasType,
     attn_mask_type: AttnMaskType,
     qkv_layout: QKVLayout,
+    softmax_type: AttnSoftmaxType,
     scaling_factor: float,
     dropout_probability: float,
     is_training: bool,
@@ -793,6 +817,7 @@ def _legacy_fused_attn(
     context_parallel_strategy: CPStrategy = CPStrategy.DEFAULT,
     context_parallel_causal_load_balanced: bool = False,
     context_parallel_axis: str = "",
+    softmax_offset: Optional[jnp.ndarray] = None,
 ):
     """
     Perform non-THD (non-packed) cuDNN fused attention.
@@ -815,6 +840,7 @@ def _legacy_fused_attn(
         seed (Optional[jnp.ndarray]): Optional random seed for dropout.
         attn_bias_type (AttnBiasType): Type of attention bias.
         attn_mask_type (AttnMaskType): Type of attention mask.
+        softmax_type (AttnSoftmaxType): Type of attention softmax.
         qkv_layout (QKVLayout): Layout of the QKV tensors.
         scaling_factor (float): Scaling factor for the attention scores.
         dropout_probability (float): Dropout probability to apply during attention.
@@ -863,10 +889,12 @@ def _legacy_fused_attn(
     output = _fused_attn(
         qkv,
         bias,
+        softmax_offset,
         SequenceDescriptor.from_seqlens((q_seq_lens, kv_seq_lens)),
         seed,
         attn_bias_type=attn_bias_type,
         attn_mask_type=attn_mask_type,
+        softmax_type=softmax_type,
         qkv_layout=qkv_layout,
         scaling_factor=scaling_factor,
         dropout_probability=dropout_probability,
@@ -900,6 +928,7 @@ def fused_attn_thd(
     context_parallel_strategy: CPStrategy = CPStrategy.DEFAULT,
     context_parallel_causal_load_balanced: bool = False,
     context_parallel_axis: str = "",
+    softmax_offset: Optional[jnp.ndarray] = None,
 ):
     """
     Deprecated THD fused attn, please use fusd_attn with SequenceDescriptor
@@ -937,6 +966,7 @@ def fused_attn_thd(
     output = _fused_attn(
         qkv,
         bias,
+        softmax_offset,
         SequenceDescriptor.from_seqlens_and_offsets(
             (q_seq_lens, kv_seq_lens), (q_seq_offsets, kv_seq_offsets)
         ),
@@ -945,6 +975,7 @@ def fused_attn_thd(
         attn_mask_type=attn_mask_type,
         qkv_layout=qkv_layout,
         scaling_factor=scaling_factor,
+        softmax_type=AttnSoftmaxType.VANILLA_SOFTMAX,
         dropout_probability=dropout_probability,
         is_training=is_training,
         max_segments_per_seq=max_segments_per_seq,
@@ -957,15 +988,17 @@ def fused_attn_thd(
     return output
 
 
-@partial(jax.custom_vjp, nondiff_argnums=(4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15))
+@partial(jax.custom_vjp, nondiff_argnums=(5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17))
 def _fused_attn(
     qkv: Tuple[jnp.ndarray, ...],
     bias: Optional[jnp.ndarray],
+    softmax_offset: Optional[jnp.ndarray],
     sequence_descriptor: SequenceDescriptor,
     seed: Optional[jnp.ndarray],
     attn_bias_type: AttnBiasType,
     attn_mask_type: AttnMaskType,
     qkv_layout: QKVLayout,
+    softmax_type: AttnSoftmaxType,
     scaling_factor: float,
     dropout_probability: float,
     is_training: bool,
@@ -979,11 +1012,13 @@ def _fused_attn(
     output, _ = _fused_attn_fwd_rule(
         qkv,
         bias,
+        softmax_offset,
         sequence_descriptor,
         seed,
         attn_bias_type,
         attn_mask_type,
         qkv_layout,
+        softmax_type,
         scaling_factor,
         dropout_probability,
         is_training,
@@ -1000,11 +1035,13 @@ def _fused_attn(
 def _fused_attn_fwd_rule(
     qkv,
     bias,
+    softmax_offset,
     sequence_descriptor,
     seed,
     attn_bias_type,
     attn_mask_type,
     qkv_layout,
+    softmax_type,
     scaling_factor,
     dropout_probability,
     is_training,
@@ -1018,10 +1055,12 @@ def _fused_attn_fwd_rule(
     output, softmax_aux, rng_state = tex.fused_attn_fwd(
         qkv,
         bias,
+        softmax_offset,
         sequence_descriptor,
         seed,
         attn_bias_type=attn_bias_type,
         attn_mask_type=attn_mask_type,
+        softmax_type=softmax_type,
         qkv_layout=qkv_layout,
         scaling_factor=scaling_factor,
         dropout_probability=dropout_probability,
@@ -1041,6 +1080,7 @@ def _fused_attn_fwd_rule(
         sequence_descriptor,
         softmax_aux,
         rng_state,
+        softmax_offset,
         output,
     )
 
@@ -1049,6 +1089,7 @@ def _fused_attn_bwd_rule(
     attn_bias_type,
     attn_mask_type,
     qkv_layout,
+    softmax_type,
     scaling_factor,
     dropout_probability,
     is_training,
@@ -1068,11 +1109,13 @@ def _fused_attn_bwd_rule(
         sequence_descriptor,
         softmax_aux,
         rng_state,
+        softmax_offset,
         output,
     ) = ctx
-    grad_qkv, grad_bias = tex.fused_attn_bwd(
+    grad_qkv, grad_bias, grad_softmax_offset = tex.fused_attn_bwd(
         qkv,
         bias,
+        softmax_offset,
         softmax_aux,
         rng_state,
         output,
@@ -1080,6 +1123,7 @@ def _fused_attn_bwd_rule(
         sequence_descriptor,
         attn_bias_type=attn_bias_type,
         attn_mask_type=attn_mask_type,
+        softmax_type=softmax_type,
         qkv_layout=qkv_layout,
         scaling_factor=scaling_factor,
         dropout_probability=dropout_probability,
@@ -1092,9 +1136,12 @@ def _fused_attn_bwd_rule(
     )
     if attn_bias_type == AttnBiasType.NO_BIAS:
         grad_bias = None
+    if softmax_type != AttnSoftmaxType.LEARNABLE_SOFTMAX:
+        grad_softmax_offset = None
     return (
         grad_qkv,
         grad_bias,
+        grad_softmax_offset,
         None,
         None,
     )
@@ -1111,6 +1158,7 @@ def fused_attn(
     attn_bias_type: AttnBiasType,
     attn_mask_type: AttnMaskType,
     qkv_layout: QKVLayout,
+    softmax_type: AttnSoftmaxType,
     scaling_factor: float,
     dropout_probability: float,
     is_training: bool,
@@ -1120,6 +1168,7 @@ def fused_attn(
     context_parallel_causal_load_balanced: bool = False,
     context_parallel_axis: str = "",
     context_checkpoint_name: str = "context",
+    softmax_offset: Optional[jnp.ndarray] = None,
 ):
     """
     Perform cuDNN fused attention.
@@ -1139,6 +1188,7 @@ def fused_attn(
         seed (Optional[jnp.ndarray]): Optional random seed for dropout.
         attn_bias_type (AttnBiasType): Type of attention bias.
         attn_mask_type (AttnMaskType): Type of attention mask.
+        softmax_type (AttnSoftmaxType): Type of attention softmax.
         qkv_layout (QKVLayout): Layout of the QKV tensors.
         scaling_factor (float): Scaling factor for the attention scores.
         dropout_probability (float): Dropout probability to apply during attention.
@@ -1153,6 +1203,9 @@ def fused_attn(
             Indicates the sequences are ordered for causal mask load balancing when running context parallelism.
         context_parallel_axis (str): The name of the context parallel axis.
         context_checkpoint_name (str): The name of the context checkpoint for the custom VJP forward pass.
+        softmax_offset (Optional[jnp.ndarray]): An optional learnable softmax offset tensor with shape
+            [1, num_heads, 1, 1]. Used when softmax_type is AttnSoftmaxType.LEARNABLE_SOFTMAX.
+            If provided, this parameter will receive gradients during backpropagation.
     Returns:
         (jnp.ndarray): The output tensor from the fused attention.
 
@@ -1200,6 +1253,7 @@ def fused_attn(
             seed,
             attn_bias_type=attn_bias_type,
             attn_mask_type=attn_mask_type,
+            softmax_type=softmax_type,
             qkv_layout=qkv_layout,
             scaling_factor=scaling_factor,
             dropout_probability=dropout_probability,
@@ -1208,15 +1262,18 @@ def fused_attn(
             context_parallel_strategy=context_parallel_strategy,
             context_parallel_causal_load_balanced=context_parallel_causal_load_balanced,
             context_parallel_axis=context_parallel_axis,
+            softmax_offset=softmax_offset,
         )
     output = _fused_attn(
         qkv,
         bias,
+        softmax_offset,
         sequence_descriptor,
         seed,
         attn_bias_type=attn_bias_type,
         attn_mask_type=attn_mask_type,
         qkv_layout=qkv_layout,
+        softmax_type=softmax_type,
         scaling_factor=scaling_factor,
         dropout_probability=dropout_probability,
         is_training=is_training,

transformer_engine/jax/cpp_extensions/activation.py

@@ -32,9 +32,9 @@ from ..sharding import all_reduce_max_along_all_axes_except_PP, all_reduce_sum_a
 from ..quantize import ScaledTensor, ScaledTensorFactory, NoScaleTensor
 from ..quantize import (
     Quantizer,
-    QuantizeLayout,
     DelayedScaleQuantizer,
     ScalingMode,
+    QuantizeLayout,
 )
 
 

transformer_engine/jax/cpp_extensions/gemm.py

@@ -39,12 +39,12 @@ from ..quantize import (
     Quantizer,
     GroupedQuantizer,
     QuantizerSet,
-    QuantizeLayout,
     noop_quantizer_set,
     is_fp8_gemm_with_all_layouts_supported,
     apply_padding_to_scale_inv,
     get_quantize_config_with_recipe,
     get_global_quantize_recipe,
+    QuantizeLayout,
 )
 from .misc import get_padded_spec, is_all_reduce_in_float32
 from ..sharding import (

transformer_engine/jax/cpp_extensions/misc.py

@@ -116,7 +116,7 @@ def multidim_transpose(shape, static_axis_boundary=-1, transpose_axis=-1):
         transpose. Note, transpose_axis should be greater than static_axis_boundary
 
     examples:
-        X in shape (dim0, dim1, dim2, dim3, dim4)
+        X of shape (dim0, dim1, dim2, dim3, dim4)
 
         static_axis_boundary == -1, transpose_axis == 2
             Xt = (dim2, dim3, dim4, dim0, dim1)

transformer_engine/jax/cpp_extensions/normalization.py

@@ -35,9 +35,9 @@ from ..sharding import (
 from ..quantize import ScaledTensor, ScaledTensorFactory, NoScaleTensor
 from ..quantize import (
     Quantizer,
-    QuantizeLayout,
     DelayedScaleQuantizer,
     ScalingMode,
+    QuantizeLayout,
 )
 
 

transformer_engine/jax/cpp_extensions/quantization.py

@@ -40,11 +40,11 @@ from ..quantize import (
     GroupedScaledTensor1x,
     Quantizer,
     GroupedQuantizer,
-    QuantizeLayout,
     ScalingMode,
     compute_scale_from_amax,
     NoScaleTensor,
     get_rht_matrix,
+    QuantizeLayout,
 )
 
 
@@ -497,6 +497,7 @@ class BaseDBiasQuantizePrimitive(BasePrimitive):
 
         x_spec = get_padded_spec(arg_infos[0])
         amax_spec = get_padded_spec(arg_infos[2])
+        sr_rng_state_spec = get_padded_spec(arg_infos[3])
         out_sharding = NamedSharding(
             mesh,
             PartitionSpec(*x_spec),
@@ -551,11 +552,14 @@ class BaseDBiasQuantizePrimitive(BasePrimitive):
         )
 
         arg_shardings = list(arg_i.sharding for arg_i in arg_infos)
-        arg_shardings[3] = NamedSharding(
-            mesh,
-            PartitionSpec(tuple(x for x in x_spec if x is not None), None),
-            desc="BaseDBiasQuantizePrimitive.sr_rng_state",
-        )
+        if len(sr_rng_state_spec) > 1:
+            # sr_rng_state shape [n_devices, state_per_device]
+            sr_rng_state_spec = (*tuple(x for x in x_spec if x is not None), None)
+            arg_shardings[3] = NamedSharding(
+                mesh,
+                PartitionSpec(*sr_rng_state_spec),
+                desc="BaseDBiasQuantizePrimitive.sr_rng_state",
+            )
         arg_shardings = tuple(arg_shardings)
         out_shardings = (
             out_sharding,
@@ -654,10 +658,12 @@ class BaseDBiasQuantizePrimitive(BasePrimitive):
         dbias = input_spec[flatten_axis:] if is_dbias else (prefix + "_dbias",)
         amax = (BATCHING + prefix + "_amax",)
         scale = (BATCHING + prefix + "_scale",)
-        sr_rng_state = (
-            BATCHING + prefix + "_sr_rng_state_partition_axis",
-            BATCHING + prefix + "sr_rng_state_data_axis",
-        )
+        sr_rng_state = (BATCHING + prefix + "_sr_rng_state",)
+        if value_types[3].shape != [0]:
+            sr_rng_state = (
+                BATCHING + prefix + "_sr_rng_state_devices",
+                prefix + "sr_rng_state_data",
+            )
 
         post_rht_amax = (BATCHING + prefix + "_post_rht_amax",)
         rht_matrix = (BATCHING + prefix + "_rht_matrix_1", BATCHING + prefix + "_rht_matrix_2")
@@ -849,7 +855,7 @@ def _quantize_dbias_impl(
     if force_1x_quantization:
         q_layout = QuantizeLayout.ROWWISE
 
-    sr_rng_state = None
+    sr_rng_state = jnp.empty((0,), jnp.uint32)
     if quantizer.scaling_mode.is_nvfp4_scaling:
         # Only NVFP4 scaling modes support stochastic rounding
         if quantizer.stochastic_rounding_rng_state is not None:
@@ -866,11 +872,7 @@ def _quantize_dbias_impl(
         x.data,
         scale,
         amax,
-        (
-            sr_rng_state
-            if sr_rng_state is not None
-            else jnp.empty((get_num_devices_in_mesh(), 1), jnp.uint32)
-        ),
+        sr_rng_state,
         post_rht_amax if post_rht_amax is not None else jnp.zeros((1,), jnp.float32),
         rht_matrix,
         out_dtype=quantizer.q_dtype,
@@ -880,7 +882,7 @@ def _quantize_dbias_impl(
         scale_dtype=quantizer.get_scale_dtype(),
         is_dbias=is_dbias if not quantizer.scaling_mode.is_nvfp4_scaling else False,
         is_outer=True,
-        stochastic_rounding=sr_rng_state is not None,
+        stochastic_rounding=sr_rng_state.size != 0,
         use_rht=use_rht,
     )
     # For DelayedScaling2x, the scale buffer is shared between rowwise and colwise

transformer_engine/jax/csrc/extensions.h

@@ -108,28 +108,28 @@ XLA_FFI_DECLARE_HANDLER_SYMBOL(FusedAttnForwardHandler);
 
 XLA_FFI_DECLARE_HANDLER_SYMBOL(FusedAttnBackwardHandler);
 
-NVTE_Fused_Attn_Backend GetFusedAttnBackend(bool is_training, DType q_dtype, DType kv_dtype,
-                                            NVTE_QKV_Layout qkv_layout, NVTE_Bias_Type bias_type,
-                                            NVTE_Mask_Type mask_type, float dropout_probability,
-                                            size_t q_num_heads, size_t kv_num_heads,
-                                            size_t q_max_seqlen, size_t kv_max_seqlen,
-                                            size_t qk_head_dim, size_t v_head_dim,
-                                            int64_t window_size_left, int64_t window_size_right);
+NVTE_Fused_Attn_Backend GetFusedAttnBackend(
+    bool is_training, DType q_dtype, DType kv_dtype, NVTE_QKV_Layout qkv_layout,
+    NVTE_Bias_Type bias_type, NVTE_Mask_Type mask_type, NVTE_Softmax_Type softmax_type,
+    float dropout_probability, size_t q_attn_heads, size_t kv_attn_heads, size_t q_max_seqlen,
+    size_t kv_max_seqlen, size_t qk_head_dim, size_t v_head_dim, int64_t window_size_left,
+    int64_t window_size_right);
 
 pybind11::tuple GetFusedAttnForwardWorkspaceSizes(
     size_t input_batch, size_t bias_batch, size_t q_max_seqlen, size_t kv_max_seqlen,
     size_t attn_heads, size_t num_gqa_groups, size_t bias_heads, size_t qk_head_dim,
     size_t v_head_dim, float scaling_factor, float dropout_probability, NVTE_Bias_Type bias_type,
-    NVTE_Mask_Type mask_type, NVTE_QKV_Layout qkv_layout, DType dtype, bool is_training,
-    size_t max_segments_per_seq, int64_t window_size_left, int64_t window_size_right);
+    NVTE_Mask_Type mask_type, NVTE_Softmax_Type softmax_type, NVTE_QKV_Layout qkv_layout,
+    DType dtype, bool is_training, size_t max_segments_per_seq, int64_t window_size_left,
+    int64_t window_size_right);
 
 pybind11::tuple GetFusedAttnBackwardWorkspaceSizes(
     size_t input_batch, size_t bias_batch, size_t q_max_seqlen, size_t kv_max_seqlen,
     size_t attn_heads, size_t num_gqa_groups, size_t bias_heads, size_t qk_head_dim,
     size_t v_head_dim, float scaling_factor, float dropout_probability, NVTE_Bias_Type bias_type,
-    NVTE_Mask_Type mask_type, NVTE_QKV_Layout qkv_layout, DType dtype, bool is_training,
-    bool deterministic, size_t max_segments_per_seq, int64_t window_size_left,
-    int64_t window_size_right);
+    NVTE_Mask_Type mask_type, NVTE_Softmax_Type softmax_type, NVTE_QKV_Layout qkv_layout,
+    DType dtype, bool is_training, bool deterministic, size_t max_segments_per_seq,
+    int64_t window_size_left, int64_t window_size_right);
 
 // GEMM
 XLA_FFI_DECLARE_HANDLER_SYMBOL(GemmHandler);