diff --git a/torchtitan/distributed/pipeline_parallel.py b/torchtitan/distributed/pipeline_parallel.py
index 8ecc7df49a..c1db39225b 100644
--- a/torchtitan/distributed/pipeline_parallel.py
+++ b/torchtitan/distributed/pipeline_parallel.py
@@ -64,6 +64,29 @@ def forward(ctx, i, w):
         @staticmethod
         def backward(ctx, grad_output):
             (w,) = ctx.saved_tensors
+            """
+            A[m,k] @ B[k,n] -> O[m,n]
+            grad_o[m,n] @ B.t()[n,k]    -> grad_a[m,k] 
+            looks right..
+            getting 
+[rank4]:[rank4]:   File "/data/users/whc/torchtitan/torchtitan/distributed/pipeline_parallel.py", line 67, in backward
+[rank4]:[rank4]:     grad_input = grad_output.mm(w.t())
+[rank4]:[rank4]:   File "/data/users/whc/torchtitan/torchtitan/distributed/pipeline_parallel.py", line 88, in split_mm
+[rank4]:[rank4]:     return MmPassThrough.apply(i1, w1)
+[rank4]:[rank4]:   File "/data/users/whc/pytorch/torch/autograd/function.py", line 583, in apply
+[rank4]:[rank4]:     return super().apply(*args, **kwargs)  # type: ignore[misc]
+[rank4]:[rank4]:   File "/data/users/whc/torchtitan/torchtitan/distributed/pipeline_parallel.py", line 74, in forward
+[rank4]:[rank4]:     return torch.mm(x, y)
+[rank4]:[rank4]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (4096x2816 and 2048x2816)
+
+[rank4]:[rank4]: RuntimeError: 
+[rank4]:[rank4]:         Failed to run stage backward:
+[rank4]:[rank4]:         Stage output: ('Tensor(torch.Size([1, 4096, 2048]), grad=True, dtype=torch.bfloat16)',)
+[rank4]:[rank4]:         Output gradient: ('Tensor(torch.Size([1, 4096, 2048]), grad=False, dtype=torch.bfloat16)',)
+[rank4]:[rank4]:         Input: ['Tensor(torch.Size([1, 4096, 2048]), grad=True, dtype=torch.bfloat16)']
+[rank4]:[rank4]:         
+            """
+            logger.error(f"MmSeparateInputGrad backward: {grad_output.shape=}, {w.t().shape=}")
             grad_input = grad_output.mm(w.t())
             return grad_input, None
 
@@ -75,13 +98,14 @@ def forward(ctx, x, y):
 
         @staticmethod
         def backward(ctx, gO):
+            # TODO(whc) - claude first wrote it this way and later tried to return None, None, i'm not sure which is correct
             return gO, gO
 
     def split_mm(i, w):
-        print("split mul")
         # Apply the pass-through node. y is passed to this node so that it can be
         # saved for backward, but detach because we don't want to actually build
         # this edge of the graph
+        logger.error(f"split_mm forward: {i.shape=}, {w.shape=}")
         w1 = MmSeparateWeightGrad.apply(i.detach(), w)
         i1 = MmSeparateInputGrad.apply(i, w.detach())
         return MmPassThrough.apply(i1, w1)
@@ -138,7 +162,6 @@ def backward(ctx, gO):
             return gO, gO, gO, None, None
 
     def split_addmm(bias, mat1, mat2, *, beta=1, alpha=1):
-        print("split addmm")
         mat2_1 = AddmmSeparateMat2Grad.apply(mat1.detach(), mat2, alpha)
         mat1_1 = AddmmSeparateMat1Grad.apply(mat1, mat2.detach(), alpha)
         bias_1 = AddmmSeparateBiasGrad.apply(bias, beta)
@@ -197,7 +220,6 @@ def backward(ctx, gO):
             return gO, None, gO, None
 
     def split_rms_norm(input, normalized_shape, weight=None, eps=None):
-        print("split rms_norm")
         weight_1 = RmsNormSeparateWeightGrad.apply(
             input.detach(), normalized_shape, weight, eps
         )
@@ -255,7 +277,6 @@ def backward(ctx, gO):
             return gO, gO, None, None, None
 
     def split_grouped_mm(input, mat2, offs=None, bias=None, out_dtype=None):
-        print("split grouped_mm")
         mat2_1 = GroupedMmSeparateMat2Grad.apply(
             input.detach(), mat2, offs, bias, out_dtype
         )
diff --git a/torchtitan/models/deepseek_v3/train_configs/deepseek_v3_16b.toml b/torchtitan/models/deepseek_v3/train_configs/deepseek_v3_16b.toml
index cb1fb30fe3..d8a38f80ae 100644
--- a/torchtitan/models/deepseek_v3/train_configs/deepseek_v3_16b.toml
+++ b/torchtitan/models/deepseek_v3/train_configs/deepseek_v3_16b.toml
@@ -61,7 +61,7 @@ export_dtype = "float32"
 async_mode = "disabled"  # ["disabled", "async", "async_with_pinned_mem]"
 
 [activation_checkpoint]
-mode = "selective"  # ["none", "selective", "full"]
+mode = "none"  # ["none", "selective", "full"]
 selective_ac_option = 'op'  # 'int' = ac every positive int layer or 'op', ac based on ops policy
 
 [compile]