Let ep_scatter support arbitrary strides / ue8m0 format

python/sglang/srt/layers/moe/ep_moe/kernels.py

@@ -813,14 +813,17 @@ def _fwd_kernel_ep_scatter_2(
     offset_in = tl.arange(0, HIDDEN_SIZE_PAD)
     mask = offset_in < HIDDEN_SIZE
 
-    offset_in_s = tl.arange(0, SCALE_HIDDEN_SIZE_PAD)
-    mask_s = offset_in_s < SCALE_HIDDEN_SIZE
+    index_in_s = tl.arange(0, SCALE_HIDDEN_SIZE_PAD)
+    mask_s = index_in_s < SCALE_HIDDEN_SIZE
 
     for token_id_int32 in range(start_token_id, total_token_num, grid_num):
         token_id = token_id_int32.to(tl.int64)
         to_copy = tl.load(recv_x + token_id * recv_x_stride0 + offset_in, mask=mask)
         to_copy_s = tl.load(
-            recv_x_scale + token_id * recv_x_scale_stride0 + offset_in_s, mask=mask_s
+            recv_x_scale
+            + token_id * recv_x_scale_stride0
+            + index_in_s * recv_x_scale_stride1,
+            mask=mask_s,
         )
 
         for topk_idx_int32 in tl.range(0, topk_num, 1, num_stages=4):
@@ -841,7 +844,11 @@ def _fwd_kernel_ep_scatter_2(
                     output_tensor_scale + dest_token_index * output_tensor_scale_stride0
                 )
                 tl.store(output_tensor_ptr + offset_in, to_copy, mask=mask)
-                tl.store(output_tensor_scale_ptr + offset_in_s, to_copy_s, mask=mask_s)
+                tl.store(
+                    output_tensor_scale_ptr + index_in_s * output_tensor_scale_stride1,
+                    to_copy_s,
+                    mask=mask_s,
+                )
 
 
 # copy from https://github.com/ModelTC/lightllm/blob/main/lightllm/common/fused_moe/deepep_scatter_gather.py
@@ -856,6 +863,7 @@ def ep_scatter(
     output_tensor_scale: torch.Tensor,
     m_indices: torch.Tensor,
     output_index: torch.Tensor,
+    scale_ue8m0: bool = False,
 ):
     BLOCK_E = 128  # token num of per expert is aligned to 128
     BLOCK_D = 128  # block size of quantization
@@ -865,7 +873,15 @@ def ep_scatter(
     # grid = (triton.cdiv(hidden_size, BLOCK_D), num_experts)
     grid = num_experts
 
+    scale_hidden_size = hidden_size // BLOCK_D
+    if scale_ue8m0:
+        # ue8m0 scales are packed here (4 scales per int32),
+        # hence the effective size of this dimension is divided by 4.
+        scale_hidden_size = ceil_div(scale_hidden_size, 4)
+
     assert m_indices.shape[0] % BLOCK_E == 0
+    assert recv_x_scale.dtype == output_tensor_scale.dtype
+    assert recv_x_scale.shape[1] == output_tensor_scale.shape[1] == scale_hidden_size
 
     _fwd_kernel_ep_scatter_1[(grid,)](
         num_recv_tokens_per_expert,
@@ -904,8 +920,8 @@ def ep_scatter(
         num_warps=num_warps,
         HIDDEN_SIZE=hidden_size,
         HIDDEN_SIZE_PAD=triton.next_power_of_2(hidden_size),
-        SCALE_HIDDEN_SIZE=hidden_size // BLOCK_D,
-        SCALE_HIDDEN_SIZE_PAD=triton.next_power_of_2(hidden_size // BLOCK_D),
+        SCALE_HIDDEN_SIZE=scale_hidden_size,
+        SCALE_HIDDEN_SIZE_PAD=triton.next_power_of_2(scale_hidden_size),
     )
     return