frameworks/slime/docker/patch/latest/megatron.patch

diff --git a/megatron/core/dist_checkpointing/strategies/common.py b/megatron/core/dist_checkpointing/strategies/common.py
index 41c21d93d..ef80f72d6 100644
--- a/megatron/core/dist_checkpointing/strategies/common.py
+++ b/megatron/core/dist_checkpointing/strategies/common.py
@@ -86,7 +86,7 @@ class TorchCommonLoadStrategy(LoadCommonStrategy):
                 msc = MultiStorageClientFeature.import_package()
                 return msc.torch.load(load_path, map_location='cpu')
             else:
-                return torch.load(load_path, map_location='cpu')
+                return torch.load(load_path, map_location='cpu', weights_only=False)
         except FileNotFoundError as e:
             err_msg = f'Common file {load_path} does not exist'
             if MultiStorageClientFeature.is_enabled():
diff --git a/megatron/core/dist_checkpointing/strategies/torch.py b/megatron/core/dist_checkpointing/strategies/torch.py
index 5a1ea308d..aa701237f 100644
--- a/megatron/core/dist_checkpointing/strategies/torch.py
+++ b/megatron/core/dist_checkpointing/strategies/torch.py
@@ -597,10 +597,12 @@ class MCoreLoadPlanner(DefaultLoadPlanner):
     def _validate_global_shapes(self, metadata, sharded_tensors):
         for sh_ten in sharded_tensors:
             if sh_ten.key not in metadata.state_dict_metadata:
-                raise KeyError(
-                    f"{sh_ten.key} from model not in state dict:"
-                    f" {sorted(metadata.state_dict_metadata.keys())}"
-                )
+                # raise KeyError(
+                #     f"{sh_ten.key} from model not in state dict:"
+                #     f" {sorted(metadata.state_dict_metadata.keys())}"
+                # )
+                print(f"{sh_ten.key} from model not in state dict, will skip")
+                continue
             loaded_shape = metadata.state_dict_metadata[sh_ten.key].size
             expected_shape = self._expected_shape(sh_ten)
             if loaded_shape != expected_shape:
@@ -630,7 +632,7 @@ class MCoreLoadPlanner(DefaultLoadPlanner):
         tensor_metadata = self.metadata.state_dict_metadata
         metadata_with_sizes = [
             (tensor_metadata[key], tensor_metadata[key].size, sharded_tensor)
-            for key, sharded_tensor in self.allow_shape_mismatch_sharded_tensors.items()
+            for key, sharded_tensor in self.allow_shape_mismatch_sharded_tensors.items() if key in tensor_metadata
         ]
         try:
             # Temporarily set sizes to expected shapes
@@ -959,6 +961,7 @@ class TorchDistLoadShardedStrategy(LoadShardedStrategy):
             planner=MCoreLoadPlanner(
                 shapes_validation_sharded_tensors=flexible_shape_sharded_tensors,
                 allow_shape_mismatch_sharded_tensors=allow_shape_mismatch_sharded_tensors,
+                allow_partial_load=True,
             ),
         )
 
diff --git a/megatron/core/extensions/transformer_engine.py b/megatron/core/extensions/transformer_engine.py
index acb93ef78..d239db4ab 100644
--- a/megatron/core/extensions/transformer_engine.py
+++ b/megatron/core/extensions/transformer_engine.py
@@ -408,6 +408,7 @@ class TELinear(te.pytorch.Linear):
         )
 
         for param in self.parameters():
+            setattr(param, "parallel_mode", parallel_mode)
             if is_expert:
                 # Reduce the gradient on the expert_data_parallel group for expert linear layers
                 setattr(param, "allreduce", not self.expert_parallel)
@@ -1161,6 +1162,61 @@ class TEDotProductAttention(te.pytorch.DotProductAttention):
 
 
 if HAVE_TE and is_te_min_version("1.9.0.dev0"):
+    def ceil_div(x: int, y: int) -> int:
+        return (x + y - 1) // y
+
+    class _FakeInt4QuantizationSTE(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, x, group_size):
+            m, n = x.shape
+            block_size_m, block_size_n = 1, group_size
+
+
+            m_padded = ceil_div(m, block_size_m) * block_size_m
+            n_padded = ceil_div(n, block_size_n) * block_size_n
+
+            x_padded = torch.zeros(
+                (m_padded, n_padded),
+                dtype=x.dtype, device=x.device
+            )
+            x_padded[:m, :n] = x
+
+            x_view = x_padded.view(
+                m_padded // block_size_m,
+                block_size_m,
+                n_padded // block_size_n,
+                block_size_n
+            )
+
+            x_max = x_view.abs().float().amax(dim=(1, 3), keepdim=True)
+            q_max = 7
+            x_scale = x_max / q_max
+
+            x_scale = x_scale.clamp(min=1e-5)
+
+            x_div = x_view / x_scale
+            x_round = torch.round(x_div)
+
+            x_q_clamped = x_round.clamp(-q_max, q_max)
+
+            x_dequant_view = x_q_clamped * x_scale
+
+            x_dequant_full = x_dequant_view.view_as(x_padded)
+            x_out = x_dequant_full[:m, :n].contiguous().to(x.dtype)
+
+            return x_out
+
+        @staticmethod
+        def backward(ctx, grad_output):
+            return grad_output, None
+
+    def fake_int4_quantization_ste(x, group_size):
+        x_out = _FakeInt4QuantizationSTE.apply(x, group_size)
+        
+        if hasattr(x, 'main_grad'):
+            x_out.main_grad = x.main_grad
+            
+        return x_out
 
     class TEGroupedLinear(te.pytorch.GroupedLinear):
         """
@@ -1351,6 +1407,7 @@ if HAVE_TE and is_te_min_version("1.9.0.dev0"):
             _is_first_microbatch = (
                 None if self.disable_parameter_transpose_cache else self.is_first_microbatch
             )
+
             out = super().forward(x, m_splits, is_first_microbatch=_is_first_microbatch)
             self.is_first_microbatch = False
 
@@ -1361,6 +1418,20 @@ if HAVE_TE and is_te_min_version("1.9.0.dev0"):
                 return out
             return out, None
 
+        def _get_weight_tensors(self):
+            """Get the weight tensors of the module."""
+            weight_tensors = super()._get_weight_tensors()
+
+            if os.getenv("OPEN_TRAINING_INT4_FAKE_QAT_FLAG", "0") == "1":
+                group_size = int(os.getenv("OPEN_TRAINING_INT4_GROUP_SIZE", "128"))
+
+                weight_tensors = [
+                    fake_int4_quantization_ste(w, group_size) 
+                    for w in weight_tensors
+                ]
+                
+            return weight_tensors
+
         def _encode_extra_state(self, state):
             # TE 2.0 changed the format of extra_state to be a byte tensor
             if is_te_min_version("2.0.0"):
diff --git a/megatron/core/fusions/fused_mla_yarn_rope_apply.py b/megatron/core/fusions/fused_mla_yarn_rope_apply.py
index 1fd5dcfae..c9aeef1f0 100644
--- a/megatron/core/fusions/fused_mla_yarn_rope_apply.py
+++ b/megatron/core/fusions/fused_mla_yarn_rope_apply.py
@@ -385,6 +385,7 @@ def rotary_fwd_kv_kernel(
     SIN,
     emb_dim: tl.constexpr,
     k_dim: tl.constexpr,
+    k_dim_ceil: tl.constexpr,
     v_dim: tl.constexpr,
     head_num: tl.constexpr,
     batch_size,
@@ -434,21 +435,27 @@ def rotary_fwd_kv_kernel(
     cos_right = tl.load(COS + token_idx * emb_dim + emb_dim // 2 + tl.arange(0, emb_dim // 2))
     sin_right = tl.load(SIN + token_idx * emb_dim + emb_dim // 2 + tl.arange(0, emb_dim // 2))
 
-    KV_ptr = KV + pid_m * stride_kv_seq + pid_head * BLOCK_H * stride_kv_nheads
-    kv_off = tl.arange(0, BLOCK_H)[:, None] * stride_kv_nheads
-    mask = kv_off < head_num * stride_kv_nheads
-    k_in_off = kv_off + tl.arange(0, k_dim)[None, :]
-    v_in_off = kv_off + k_dim + tl.arange(0, v_dim)[None, :]
-    k = tl.load(KV_ptr + k_in_off, mask=mask)
-    v = tl.load(KV_ptr + v_in_off, mask=mask)
+    KV_ptr = KV + pid_m * stride_kv_seq # + pid_head * BLOCK_H * stride_kv_nheads
+    ki_range = tl.arange(0, BLOCK_H)[:, None] + pid_head * BLOCK_H
+    kj_range = tl.arange(0, k_dim_ceil)[None, :]
+    mask_k = (ki_range < head_num) & (kj_range < k_dim)
+    mask_v = ki_range < head_num
+    k_off = ki_range * stride_kv_nheads + kj_range
+    if v_dim > 0:
+        v_off = ki_range * stride_kv_nheads + k_dim + tl.arange(0, v_dim)[None, :]
+        v = tl.load(KV_ptr + v_off, mask=mask_v)
+    else:
+        v = tl.zeros((BLOCK_H, 1), dtype=KV.dtype.element_ty)
+    k = tl.load(KV_ptr + k_off, mask=mask_k)
 
-    K_ptr = O_KEY + pid_m * stride_k_seq + pid_head * BLOCK_H * stride_k_nheads
-    V_ptr = O_VALUE + pid_m * stride_v_seq + pid_head * BLOCK_H * stride_v_nheads
+    K_ptr = O_KEY + pid_m * stride_k_seq # + pid_head * BLOCK_H * stride_k_nheads
+    V_ptr = O_VALUE + pid_m * stride_v_seq # + pid_head * BLOCK_H * stride_v_nheads
 
-    k_out_off = tl.arange(0, BLOCK_H)[:, None] * stride_k_nheads + tl.arange(0, k_dim)[None, :]
-    v_out_off = tl.arange(0, BLOCK_H)[:, None] * stride_v_nheads + tl.arange(0, v_dim)[None, :]
-    tl.store(K_ptr + k_out_off, k, mask=mask)
-    tl.store(V_ptr + v_out_off, v, mask=mask)
+    k_out_off = ki_range * stride_k_nheads + kj_range
+    tl.store(K_ptr + k_out_off, k, mask=mask_k)
+    if v_dim > 0:
+        v_out_off = ki_range * stride_v_nheads + tl.arange(0, v_dim)[None, :]
+        tl.store(V_ptr + v_out_off, v, mask=mask_v)
 
     EMB = K_POS_EMB + pid_m * stride_emb_seq
     # x1 = t[..., 0::2], x2 = t[..., 1::2]
@@ -460,14 +467,16 @@ def rotary_fwd_kv_kernel(
     x_left = x_left.expand_dims(0).broadcast_to(BLOCK_H, emb_dim // 2)
     x_right = x_right.expand_dims(0).broadcast_to(BLOCK_H, emb_dim // 2)
 
+    x_range = tl.arange(0, BLOCK_H)[:, None] + pid_head * BLOCK_H
+    mask_x = x_range < head_num
     x_left_off = (
-        tl.arange(0, BLOCK_H)[:, None] * stride_k_nheads
+        x_range * stride_k_nheads
         + k_dim
         + tl.arange(0, emb_dim // 2)[None, :]
     )
     x_right_off = x_left_off + emb_dim // 2
-    tl.store(K_ptr + x_left_off, x_left, mask=mask)
-    tl.store(K_ptr + x_right_off, x_right, mask=mask)
+    tl.store(K_ptr + x_left_off, x_left, mask=mask_x)
+    tl.store(K_ptr + x_right_off, x_right, mask=mask_x)
 
 
 @triton.autotune(
@@ -493,6 +502,7 @@ def rotary_bwd_kv_kernel(
     SIN,
     emb_dim: tl.constexpr,
     k_dim: tl.constexpr,
+    k_dim_ceil: tl.constexpr,
     v_dim: tl.constexpr,
     head_num: tl.constexpr,
     batch_size,
@@ -533,27 +543,32 @@ def rotary_bwd_kv_kernel(
     else:
         token_idx = _get_thd_token_idx(cu_seqlens_kv, pid_m, seq_num, cp_rank, cp_size)
 
-    dKV_ptr = dKV + pid_m * stride_dkv_seq + pid_head * BLOCK_H * stride_dkv_nheads
-    dkv_off = tl.arange(0, BLOCK_H)[:, None] * stride_dkv_nheads
-    mask = dkv_off < head_num * stride_dkv_nheads
-    dk_out_off = dkv_off + tl.arange(0, k_dim)[None, :]
-    dv_out_off = dkv_off + k_dim + tl.arange(0, v_dim)[None, :]
-
-    dK_ptr = dK + pid_m * stride_dk_seq + pid_head * BLOCK_H * stride_dk_nheads
-    dV_ptr = dV + pid_m * stride_dv_seq + pid_head * BLOCK_H * stride_dv_nheads
-    dk_in_off = tl.arange(0, BLOCK_H)[:, None] * stride_dk_nheads + tl.arange(0, k_dim)[None, :]
-    dv_in_off = tl.arange(0, BLOCK_H)[:, None] * stride_dv_nheads + tl.arange(0, v_dim)[None, :]
-    dk = tl.load(dK_ptr + dk_in_off, mask=mask)
-    dv = tl.load(dV_ptr + dv_in_off, mask=mask)
-    tl.store(dKV_ptr + dk_out_off, dk, mask=mask)
-    tl.store(dKV_ptr + dv_out_off, dv, mask=mask)
+    dKV_ptr = dKV + pid_m * stride_dkv_seq # + pid_head * BLOCK_H * stride_dkv_nheads
+    ki_range = tl.arange(0, BLOCK_H)[:, None] + pid_head * BLOCK_H 
+    kj_range = tl.arange(0, k_dim_ceil)[None, :]
+    mask_k = (ki_range < head_num) & (kj_range < k_dim)
+    mask_v = ki_range < head_num
+    dk_out_off = ki_range * stride_dkv_nheads + kj_range
+
+    dK_ptr = dK + pid_m * stride_dk_seq # + pid_head * BLOCK_H * stride_dk_nheads
+    dV_ptr = dV + pid_m * stride_dv_seq # + pid_head * BLOCK_H * stride_dv_nheads
+    dk_in_off = ki_range * stride_dk_nheads + kj_range
+
+    dk = tl.load(dK_ptr + dk_in_off, mask=mask_k)
+    tl.store(dKV_ptr + dk_out_off, dk, mask=mask_k)
+    
+    if v_dim > 0:
+        dv_out_off = ki_range * stride_dkv_nheads + k_dim + tl.arange(0, v_dim)[None, :]
+        dv_in_off = ki_range * stride_dv_nheads + tl.arange(0, v_dim)[None, :]
+        dv = tl.load(dV_ptr + dv_in_off, mask=mask_v)
+        tl.store(dKV_ptr + dv_out_off, dv, mask=mask_v)
 
     if pid_head == 0:
         x_left_accum = tl.zeros((BLOCK_H, emb_dim // 2), dtype=tl.float32)
         x_right_accum = tl.zeros((BLOCK_H, emb_dim // 2), dtype=tl.float32)
         for i in tl.static_range(triton.cdiv(head_num, BLOCK_H)):
-            dK_ptr = dK + pid_m * stride_dk_seq + i * BLOCK_H * stride_dk_nheads
-            x_off = tl.arange(0, BLOCK_H)[:, None] * stride_dk_nheads + k_dim
+            dK_ptr = dK + pid_m * stride_dk_seq # + i * BLOCK_H * stride_dk_nheads
+            x_off = tl.arange(0, BLOCK_H)[:, None] * stride_dk_nheads + k_dim + i * BLOCK_H * stride_dk_nheads
             mask = x_off < head_num * stride_dk_nheads
             x_left_off = x_off + tl.arange(0, emb_dim // 2)[None, :]
             x_right_off = x_left_off + emb_dim // 2
@@ -632,6 +647,7 @@ class ApplyMLARotaryEmbKV(torch.autograd.Function):
 
         o_key = kv.new_empty(total_seqlen, nheads, emb_dim + k_dim)
         o_value = kv.new_empty(total_seqlen, nheads, v_dim)
+        k_dim_ceil = triton.next_power_of_2(k_dim)
 
         grid = lambda META: (total_seqlen, triton.cdiv(nheads, META["BLOCK_H"]))
         rotary_fwd_kv_kernel[grid](
@@ -643,6 +659,7 @@ class ApplyMLARotaryEmbKV(torch.autograd.Function):
             sin,
             emb_dim,
             k_dim,
+            k_dim_ceil,
             v_dim,
             nheads,
             batch_size,
@@ -700,6 +717,7 @@ class ApplyMLARotaryEmbKV(torch.autograd.Function):
 
         d_kv = dk.new_empty(total_seqlen, nheads, ctx.k_dim + ctx.v_dim)
         d_emb = dk.new_empty(total_seqlen, 1, ctx.emb_dim)
+        k_dim_ceil = triton.next_power_of_2(ctx.k_dim)
 
         grid = lambda META: (total_seqlen, triton.cdiv(nheads, META["BLOCK_H"]))
         rotary_bwd_kv_kernel[grid](
@@ -711,6 +729,7 @@ class ApplyMLARotaryEmbKV(torch.autograd.Function):
             sin,
             ctx.emb_dim,
             ctx.k_dim,
+            k_dim_ceil,
             ctx.v_dim,
             nheads,
             batch_size,
diff --git a/megatron/core/models/common/embeddings/rotary_pos_embedding.py b/megatron/core/models/common/embeddings/rotary_pos_embedding.py
index 5d7b69cd3..2e0a26815 100644
--- a/megatron/core/models/common/embeddings/rotary_pos_embedding.py
+++ b/megatron/core/models/common/embeddings/rotary_pos_embedding.py
@@ -348,6 +348,7 @@ class MultimodalRotaryEmbedding(nn.Module):
 
         # shape (seq_length, bs, 1, 2 * dim)
         emb = emb[..., None, :].transpose(0, 1).contiguous()
+        packed_seq = packed_seq_params is not None and packed_seq_params.qkv_format == 'thd'
         if packed_seq_params is not None and packed_seq_params.local_cp_size is not None:
             if packed_seq_params.local_cp_size > 1:
                 # Set CP group to dynamic CP group for CP slicing
@@ -357,7 +358,9 @@ class MultimodalRotaryEmbedding(nn.Module):
                 cp_group = None
         else:
             cp_group = self.cp_group
-        if cp_group is not None and cp_group.size() > 1:
+        # For THD (packed sequence) format, skip CP slicing here — it is handled
+        # per-sequence inside _apply_rotary_pos_emb_thd instead (same as RotaryEmbedding).
+        if cp_group is not None and cp_group.size() > 1 and not packed_seq:
             # slice rotary_pos_emb along sequence dimension and select the parition of the current
             # CP rank
             emb = get_pos_emb_on_this_cp_rank(emb, 0, cp_group)
diff --git a/megatron/core/models/common/language_module/language_module.py b/megatron/core/models/common/language_module/language_module.py
index 13d74aa52..060898a7a 100644
--- a/megatron/core/models/common/language_module/language_module.py
+++ b/megatron/core/models/common/language_module/language_module.py
@@ -184,7 +184,15 @@ class LanguageModule(MegatronModule):
             assert (
                 column_parallel_linear is not None
             ), "column_parallel_linear cannot be None when not using fused linear cross entropy."
-            logits, _ = column_parallel_linear(hidden, **col_linear_kwargs)
+            # output
+            output_layer_params = {k: v.detach() for k, v in column_parallel_linear.named_parameters()}
+            output_layer_buffers = dict(column_parallel_linear.named_buffers())
+            logits, _ = torch.func.functional_call(
+                column_parallel_linear,
+                {**output_layer_params, **output_layer_buffers},
+                (hidden,),
+                col_linear_kwargs,
+            )
 
             return self.compute_language_model_loss(labels, logits)
 
diff --git a/megatron/core/models/gpt/gpt_layer_specs.py b/megatron/core/models/gpt/gpt_layer_specs.py
index e21127b87..712793853 100755
--- a/megatron/core/models/gpt/gpt_layer_specs.py
+++ b/megatron/core/models/gpt/gpt_layer_specs.py
@@ -188,6 +188,8 @@ def get_gpt_layer_with_transformer_engine_spec(
     use_kitchen: bool = False,
     use_te_activation_func: bool = False,
     fallback_to_eager_attn: bool = False,
+    post_self_attn_layernorm: bool = False,
+    post_mlp_layernorm: bool = False,
 ) -> ModuleSpec:
     """Use this spec to use lower-level Transformer Engine modules (required for fp8 training).
 
@@ -260,6 +262,8 @@ def get_gpt_layer_with_transformer_engine_spec(
         mlp=mlp,
         sharded_state_dict_keys_map=sharded_state_dict_keys_map,
         normalization=normalization,
+        post_self_attn_layernorm=post_self_attn_layernorm,
+        post_mlp_layernorm=post_mlp_layernorm,
     )
 
 
@@ -349,6 +353,8 @@ def get_transformer_layer_spec_for_backend(
     mlp: ModuleSpec,
     sharded_state_dict_keys_map: Optional[dict] = None,
     normalization: Optional[str] = None,
+    post_self_attn_layernorm: bool = False,
+    post_mlp_layernorm: bool = False,
 ) -> ModuleSpec:
     """Helper function to get module spec for TransformerLayer"""
 
@@ -371,9 +377,11 @@ def get_transformer_layer_spec_for_backend(
             input_layernorm=input_layernorm,
             self_attention=attention,
             self_attn_bda=get_bias_dropout_add,
+            post_self_attn_layernorm=TENorm if post_self_attn_layernorm else IdentityOp,
             pre_mlp_layernorm=pre_mlp_layernorm,
             mlp=mlp,
             mlp_bda=get_bias_dropout_add,
+            post_mlp_layernorm=TENorm if post_mlp_layernorm else IdentityOp,
             sharded_state_dict_keys_map=sharded_state_dict_keys_map,
         ),
     )
diff --git a/megatron/core/models/gpt/gpt_model.py b/megatron/core/models/gpt/gpt_model.py
index a1230568c..1fd52f65a 100644
--- a/megatron/core/models/gpt/gpt_model.py
+++ b/megatron/core/models/gpt/gpt_model.py
@@ -446,6 +446,7 @@ class GPTModel(LanguageModule):
         *,
         inference_params: Optional[BaseInferenceContext] = None,
         loss_mask: Optional[Tensor] = None,
+        mtp_kwargs: Optional[dict] = {},
     ) -> Tensor:
         """Forward function of the GPT Model This function passes the input tensors
         through the embedding layer, and then the decoder and finally into the post
@@ -508,6 +509,7 @@ class GPTModel(LanguageModule):
             runtime_gather_output=runtime_gather_output,
             extra_block_kwargs=extra_block_kwargs,
             inference_context=inference_context,
+            mtp_kwargs=mtp_kwargs,
         )
 
     def _postprocess(
@@ -529,6 +531,7 @@ class GPTModel(LanguageModule):
         runtime_gather_output=None,
         extra_block_kwargs=None,
         inference_context=None,
+        mtp_kwargs={},
     ):
         """Postprocesses decoder hidden states to generate logits or compute loss.
 
@@ -543,7 +546,8 @@ class GPTModel(LanguageModule):
         output_weight = None
         if self.share_embeddings_and_output_weights:
             output_weight = self.shared_embedding_or_output_weight()
-        if mtp_in_postprocess:
+
+        if mtp_in_postprocess and mtp_kwargs.get('mtp_labels', None) is not None:
             hidden_states = self.mtp(
                 input_ids=input_ids,
                 position_ids=position_ids,
@@ -563,13 +567,18 @@ class GPTModel(LanguageModule):
             return hidden_states
 
         # Skip when mtp_num_layers is None or 0
-        if self.config.mtp_num_layers:
-            mtp_labels = labels.clone()
+        if self.config.mtp_num_layers and mtp_kwargs.get('mtp_labels', None) is not None:
+            mtp_labels = mtp_kwargs['mtp_labels'].clone()
+            mtp_labels, _ = roll_tensor(mtp_labels, shifts=-1, dims=-1, cp_group=self.cp_group, packed_seq_params=packed_seq_params)
+
             hidden_states_list = torch.chunk(hidden_states, 1 + self.config.mtp_num_layers, dim=0)
             hidden_states = hidden_states_list[0]
             if loss_mask is None:
                 # if loss_mask is not provided, use all ones as loss_mask
                 loss_mask = torch.ones_like(mtp_labels)
+            else:
+                # Otherwise, roll the loss_mask to keep up with the mtp_labels
+                loss_mask, _ = roll_tensor(loss_mask, shifts=-1, dims=-1, cp_group=self.cp_group, packed_seq_params=packed_seq_params)
             for mtp_layer_number in range(self.config.mtp_num_layers):
                 # Calc loss for the current Multi-Token Prediction (MTP) layers.
                 mtp_labels, _ = roll_tensor(
@@ -595,7 +604,7 @@ class GPTModel(LanguageModule):
                     sequence_parallel_enabled=self.output_layer.sequence_parallel,
                     column_parallel_linear=self.output_layer,
                     col_linear_kwargs={
-                        'weight': output_weight,
+                        'weight': output_weight.detach() if output_weight else None,
                         'runtime_gather_output': runtime_gather_output,
                     },
                 )
diff --git a/megatron/core/optimizer/distrib_optimizer.py b/megatron/core/optimizer/distrib_optimizer.py
index 6e093f96f..eac21a3ea 100644
--- a/megatron/core/optimizer/distrib_optimizer.py
+++ b/megatron/core/optimizer/distrib_optimizer.py
@@ -677,6 +677,8 @@ class DistributedOptimizer(MixedPrecisionOptimizer):
                 # TE FusedAdam will not accumulate step for empty param groups, so we need to
                 # align the step across param groups.
                 param_group["step"] = int(step)
+            if "step" in param_group and param_group["step"] is None:
+                del param_group["step"]
 
         # Grad scaler state.
         if self.grad_scaler:
@@ -1646,6 +1648,8 @@ class DistributedOptimizer(MixedPrecisionOptimizer):
                             if key == 'padding':
                                 tensors[key] = LocalNonpersistentObject(tensors[key])
                                 continue
+                            if key == 'step':
+                                continue
                             assert tensors[key].shape == (gbuf_local_end - gbuf_local_start,), (
                                 tensors[key].shape,
                                 gbuf_local_start,
diff --git a/megatron/core/parallel_state.py b/megatron/core/parallel_state.py
index a273002b9..4f821cfd5 100644
--- a/megatron/core/parallel_state.py
+++ b/megatron/core/parallel_state.py
@@ -11,6 +11,7 @@ from typing import Callable, List, Optional
 
 import numpy as np
 import torch
+import torch.distributed as dist
 
 from .utils import GlobalMemoryBuffer, is_torch_min_version
 
diff --git a/megatron/core/pipeline_parallel/p2p_communication.py b/megatron/core/pipeline_parallel/p2p_communication.py
index ac839c21f..f18309217 100644
--- a/megatron/core/pipeline_parallel/p2p_communication.py
+++ b/megatron/core/pipeline_parallel/p2p_communication.py
@@ -26,22 +26,22 @@ def _batched_p2p_ops(
     ops = []
     if tensor_send_prev is not None:
         send_prev_op = torch.distributed.P2POp(
-            torch.distributed.isend, tensor_send_prev, prev_pipeline_rank, group
+            torch.distributed.isend, tensor_send_prev, prev_pipeline_rank,
         )
         ops.append(send_prev_op)
     if tensor_recv_prev is not None:
         recv_prev_op = torch.distributed.P2POp(
-            torch.distributed.irecv, tensor_recv_prev, prev_pipeline_rank, group
+            torch.distributed.irecv, tensor_recv_prev, prev_pipeline_rank,
         )
         ops.append(recv_prev_op)
     if tensor_send_next is not None:
         send_next_op = torch.distributed.P2POp(
-            torch.distributed.isend, tensor_send_next, next_pipeline_rank, group
+            torch.distributed.isend, tensor_send_next, next_pipeline_rank,
         )
         ops.append(send_next_op)
     if tensor_recv_next is not None:
         recv_next_op = torch.distributed.P2POp(
-            torch.distributed.irecv, tensor_recv_next, next_pipeline_rank, group
+            torch.distributed.irecv, tensor_recv_next, next_pipeline_rank,
         )
         ops.append(recv_next_op)
     if len(ops) > 0:
diff --git a/megatron/core/transformer/moe/moe_utils.py b/megatron/core/transformer/moe/moe_utils.py
index 28cff06f5..58dc4bb70 100644
--- a/megatron/core/transformer/moe/moe_utils.py
+++ b/megatron/core/transformer/moe/moe_utils.py
@@ -587,6 +587,9 @@ def topk_routing_with_score_function(
         else:
             return torch.topk(scores, k=topk, dim=1)
 
+    from slime.utils.routing_replay import get_routing_replay_compute_topk
+    compute_topk = get_routing_replay_compute_topk(compute_topk)
+
     if score_function == "softmax":
         if use_pre_softmax:
             scores = torch.softmax(logits, dim=-1, dtype=torch.float32).type_as(logits)
diff --git a/megatron/core/transformer/moe/router.py b/megatron/core/transformer/moe/router.py
index 16fc9d9af..517944f25 100644
--- a/megatron/core/transformer/moe/router.py
+++ b/megatron/core/transformer/moe/router.py
@@ -201,6 +201,9 @@ class TopKRouter(Router):
             self.global_tokens_per_expert = None
             self.ga_steps = None
 
+        from slime.utils.routing_replay import register_routing_replay
+        register_routing_replay(self)
+
     def _maintain_float32_expert_bias(self):
         """
         Maintain the expert bias in float32.
diff --git a/megatron/core/transformer/multi_token_prediction.py b/megatron/core/transformer/multi_token_prediction.py
index a8f4abfcd..f33f6f05e 100755
--- a/megatron/core/transformer/multi_token_prediction.py
+++ b/megatron/core/transformer/multi_token_prediction.py
@@ -6,6 +6,7 @@ from typing import Callable, List, Optional, Union
 
 import torch
 from torch import Tensor
+import warnings
 
 from megatron.core import InferenceParams, parallel_state, tensor_parallel
 from megatron.core.dist_checkpointing.mapping import ShardedStateDict
@@ -714,17 +715,19 @@ class MultiTokenPredictionLayer(MegatronModule):
             cp_group=self.cp_group,
             packed_seq_params=packed_seq_params,
         )
-        position_ids, _ = roll_tensor(
-            position_ids,
-            shifts=-1,
-            dims=-1,
-            cp_group=self.cp_group,
-            packed_seq_params=packed_seq_params,
-        )
+        if position_ids is not None:
+            position_ids, _ = roll_tensor(
+                position_ids,
+                shifts=-1,
+                dims=-1,
+                cp_group=self.cp_group,
+                packed_seq_params=packed_seq_params,
+            )
         # embedding
         decoder_input = embedding(input_ids=input_ids, position_ids=position_ids)
+        decoder_input = decoder_input.detach()
 
-        hidden_states = make_viewless_tensor(inp=hidden_states, requires_grad=True, keep_graph=True)
+        hidden_states = make_viewless_tensor(inp=hidden_states, requires_grad=True, keep_graph=False)
 
         return input_ids, position_ids, decoder_input, hidden_states
 
@@ -826,6 +829,51 @@ class MultiTokenPredictionLayer(MegatronModule):
         return hidden_states
 
     def _checkpointed_forward(self, forward_func, *args, **kwargs):
+        """Wrap `forward_func` with activation checkpointing while only passing tensors.
+
+        Non-tensor arguments (e.g., configuration objects, None) are captured via closure so
+        that checkpoint implementations never receive them directly, avoiding save_for_backward
+        issues with non-tensor inputs.
+        """
+
+        # TODO(jiajun): Is there any better implementation here?
+        positional_specs = []
+        kw_specs = []
+        tensor_args: List[torch.Tensor] = []
+
+        for arg in args:
+            if torch.is_tensor(arg):
+                positional_specs.append(('tensor', len(tensor_args)))
+                tensor_args.append(arg)
+            else:
+                positional_specs.append(('const', arg))
+
+        for key, value in kwargs.items():
+            if torch.is_tensor(value):
+                kw_specs.append((key, ('tensor', len(tensor_args))))
+                tensor_args.append(value)
+            else:
+                kw_specs.append((key, ('const', value)))
+
+        def run(*flat_tensor_args):
+            rebuilt_args = []
+            for spec_type, payload in positional_specs:
+                if spec_type == 'tensor':
+                    rebuilt_args.append(flat_tensor_args[payload])
+                else:
+                    rebuilt_args.append(payload)
+
+            rebuilt_kwargs = {}
+            for key, (spec_type, payload) in kw_specs:
+                if spec_type == 'tensor':
+                    rebuilt_kwargs[key] = flat_tensor_args[payload]
+                else:
+                    rebuilt_kwargs[key] = payload
+
+            return forward_func(*rebuilt_args, **rebuilt_kwargs)
+
+        tensor_args_tuple = tuple(tensor_args)
+
         def checkpoint_handler():
             """Determines whether to use the `te_checkpoint` or `tensor_parallel.checkpoint`"""
             if self.config.fp8:
@@ -836,12 +884,11 @@ class MultiTokenPredictionLayer(MegatronModule):
                     self.config.distribute_saved_activations,
                     tensor_parallel.random.get_cuda_rng_tracker,
                     parallel_state.get_tensor_model_parallel_group(),
-                    *args,
-                    **kwargs,
+                    *tensor_args_tuple,
                 )
             else:
                 return tensor_parallel.checkpoint(
-                    forward_func, self.config.distribute_saved_activations, *args, *kwargs.values()
+                    run, self.config.distribute_saved_activations, *tensor_args_tuple
                 )
 
         if self.config.recompute_method == 'uniform':
diff --git a/megatron/core/transformer/transformer_config.py b/megatron/core/transformer/transformer_config.py
index e2705bd9f..a0aa109b5 100644
--- a/megatron/core/transformer/transformer_config.py
+++ b/megatron/core/transformer/transformer_config.py
@@ -210,6 +210,9 @@ class TransformerConfig(ModelParallelConfig):
     attention_output_gate: bool = False
     """Whether to apply output gate to the attention layers."""
 
+    post_self_attn_layernorm: bool = False
+    post_mlp_layernorm: bool = False
+
     test_mode: bool = False
     """Whether to run real-time tests."""
 
diff --git a/megatron/core/transformer/transformer_layer.py b/megatron/core/transformer/transformer_layer.py
index 3ea405770..5a42001b9 100644
--- a/megatron/core/transformer/transformer_layer.py
+++ b/megatron/core/transformer/transformer_layer.py
@@ -223,6 +223,7 @@ class TransformerLayerSubmodules:
     input_layernorm: Union[ModuleSpec, type] = IdentityOp
     self_attention: Union[ModuleSpec, type] = IdentityOp
     self_attn_bda: Union[ModuleSpec, type] = IdentityFuncOp
+    post_self_attn_layernorm: Union[ModuleSpec, type] = IdentityOp
 
     pre_cross_attn_layernorm: Union[ModuleSpec, type] = IdentityOp
     cross_attention: Union[ModuleSpec, type] = IdentityOp
@@ -231,6 +232,7 @@ class TransformerLayerSubmodules:
     pre_mlp_layernorm: Union[ModuleSpec, type] = IdentityOp
     mlp: Union[ModuleSpec, type] = IdentityOp
     mlp_bda: Union[ModuleSpec, type] = IdentityFuncOp
+    post_mlp_layernorm: Union[ModuleSpec, type] = IdentityOp
 
     # Mapping for sharded tensor keys to be applied in `sharded_state_dict` method
     sharded_state_dict_keys_map: Dict[str, str] = field(default_factory=dict)
@@ -310,6 +312,13 @@ class TransformerLayer(GraphableMegatronModule, BaseTransformerLayer):
         # [Module 3: BiasDropoutFusion]
         self.self_attn_bda = build_module(submodules.self_attn_bda)
 
+        self.post_self_attn_layernorm = build_module(
+            submodules.post_self_attn_layernorm,
+            config=self.config,
+            hidden_size=self.config.hidden_size,
+            eps=self.config.layernorm_epsilon,
+        )
+
         # [Module 4: Post SelfAttention] Optional Layernorm after self-attn
         self.pre_cross_attn_layernorm = build_module(
             submodules.pre_cross_attn_layernorm,
@@ -375,6 +384,13 @@ class TransformerLayer(GraphableMegatronModule, BaseTransformerLayer):
 
         self.is_moe_layer = isinstance(self.mlp, MoELayer)
 
+        self.post_mlp_layernorm = build_module(
+            submodules.post_mlp_layernorm,
+            config=self.config,
+            hidden_size=self.config.hidden_size,
+            eps=self.config.layernorm_epsilon
+        )
+
         self.recompute_input_layernorm = False
         self.recompute_pre_mlp_layernorm = False
         self.recompute_mlp = False
@@ -551,6 +567,10 @@ class TransformerLayer(GraphableMegatronModule, BaseTransformerLayer):
                 attention_output_with_bias[0]
             )
 
+        attention_output, attention_output_bias = attention_output_with_bias
+        attention_output = self.post_self_attn_layernorm(attention_output)
+        attention_output_with_bias = (attention_output, attention_output_bias)
+
         # TODO: could we move `bias_dropout_add_exec_handler` itself
         # inside the module provided in the `bias_dropout_add_spec` module?
         nvtx_range_push(suffix="self_attn_bda")
@@ -677,6 +697,10 @@ class TransformerLayer(GraphableMegatronModule, BaseTransformerLayer):
         else:
             mlp_output_with_bias = self.mlp(pre_mlp_layernorm_output)
 
+        mlp_output, mlp_output_bias = mlp_output_with_bias
+        mlp_output = self.post_mlp_layernorm(mlp_output)
+        mlp_output_with_bias = (mlp_output, mlp_output_bias)
+
         if self.recompute_pre_mlp_layernorm:
             # discard the output of the pre-mlp layernorm and register the recompute
             # as a gradient hook of mlp_output_with_bias[0]
diff --git a/megatron/training/arguments.py b/megatron/training/arguments.py
index b267c8a81..83736acdc 100644
--- a/megatron/training/arguments.py
+++ b/megatron/training/arguments.py
@@ -1398,6 +1398,9 @@ def core_transformer_config_from_args(args, config_class=None):
 
     kw_args['inference_sampling_seed'] = args.seed
 
+    kw_args['post_self_attn_layernorm'] = args.post_self_attn_layernorm
+    kw_args['post_mlp_layernorm'] = args.post_mlp_layernorm
+
     # handle quantization config
     # NOTE: Kitchen arguments are only added to the namespace when
     # Kitchen library is available.
@@ -1764,6 +1767,12 @@ def _add_network_size_args(parser):
                        action='store_true',
                        help='If set, use original BERT residula connection '
                        'ordering.')
+    group.add_argument('--post-self-attn-layernorm', action='store_true',
+                       help='If set, use post self attention layernorm.')
+    group.add_argument('--post-mlp-layernorm', action='store_true',
+                       help='If set, use post MLP layernorm.')
+    group.add_argument('--use-gated-attention', action='store_true',
+                       help='If set, use gated attention as in Qwen3Next')
     group.add_argument('--openai-gelu', action='store_true',
                        help='Use OpenAIs GeLU implementation. This option'
                        'should not be used unless for backward compatibility'
diff --git a/megatron/training/tokenizer/tokenizer.py b/megatron/training/tokenizer/tokenizer.py
index 13b7526ca..6c590f653 100644
--- a/megatron/training/tokenizer/tokenizer.py
+++ b/megatron/training/tokenizer/tokenizer.py
@@ -136,7 +136,7 @@ class _HuggingFaceTokenizer(MegatronLegacyTokenizer):
         # TODO(bnorick): download tokenizer once to lustre and use force offline to make sure all tasks read it from there
         self._tokenizer = transformers.AutoTokenizer.from_pretrained(
             pretrained_model_name_or_path=pretrained_model_name_or_path,
-            trust_remote_code=trust_remote_code,
+            trust_remote_code=True,
             **kwargs,
         )
         self._vocab = self._tokenizer.get_vocab()