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kernels-bot commited on Aug 25

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703da04

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build/torch211-cxx11-cu126-aarch64-linux/{_megablocks_cuda_18e92fb.abi3.so → _megablocks_cuda_c4d0dc5.abi3.so} +1 -1
build/torch211-cxx11-cu126-aarch64-linux/_ops.py +3 -3
build/torch211-cxx11-cu126-aarch64-linux/metadata.json +5 -5
build/torch211-cxx11-cu126-aarch64-linux/metadata.json.sigstore +1 -1
build/torch211-cxx11-cu126-aarch64-linux/xpu_fused_moe.py +22 -12
build/torch211-cxx11-cu128-aarch64-linux/{_megablocks_cuda_18e92fb.abi3.so → _megablocks_cuda_c4d0dc5.abi3.so} +1 -1
build/torch211-cxx11-cu128-aarch64-linux/_ops.py +3 -3
build/torch211-cxx11-cu128-aarch64-linux/metadata.json +5 -5
build/torch211-cxx11-cu128-aarch64-linux/metadata.json.sigstore +1 -1
build/torch211-cxx11-cu128-aarch64-linux/xpu_fused_moe.py +22 -12
build/torch211-cxx11-cu130-aarch64-linux/{_megablocks_cuda_18e92fb.abi3.so → _megablocks_cuda_c4d0dc5.abi3.so} +1 -1
build/torch211-cxx11-cu130-aarch64-linux/_ops.py +3 -3
build/torch211-cxx11-cu130-aarch64-linux/metadata.json +5 -5
build/torch211-cxx11-cu130-aarch64-linux/metadata.json.sigstore +1 -1
build/torch211-cxx11-cu130-aarch64-linux/xpu_fused_moe.py +22 -12
build/torch212-cxx11-cu126-aarch64-linux/{_megablocks_cuda_18e92fb.abi3.so → _megablocks_cuda_c4d0dc5.abi3.so} +1 -1
build/torch212-cxx11-cu126-aarch64-linux/_ops.py +3 -3
build/torch212-cxx11-cu126-aarch64-linux/metadata.json +5 -5
build/torch212-cxx11-cu126-aarch64-linux/metadata.json.sigstore +1 -1
build/torch212-cxx11-cu126-aarch64-linux/xpu_fused_moe.py +22 -12
build/torch212-cxx11-cu130-aarch64-linux/{_megablocks_cuda_18e92fb.abi3.so → _megablocks_cuda_c4d0dc5.abi3.so} +1 -1
build/torch212-cxx11-cu130-aarch64-linux/_ops.py +3 -3
build/torch212-cxx11-cu130-aarch64-linux/metadata.json +5 -5
build/torch212-cxx11-cu130-aarch64-linux/metadata.json.sigstore +1 -1
build/torch212-cxx11-cu130-aarch64-linux/xpu_fused_moe.py +22 -12
build/torch212-cxx11-cu132-aarch64-linux/{_megablocks_cuda_18e92fb.abi3.so → _megablocks_cuda_c4d0dc5.abi3.so} +1 -1
build/torch212-cxx11-cu132-aarch64-linux/_ops.py +3 -3
build/torch212-cxx11-cu132-aarch64-linux/metadata.json +5 -5
build/torch212-cxx11-cu132-aarch64-linux/metadata.json.sigstore +1 -1
build/torch212-cxx11-cu132-aarch64-linux/xpu_fused_moe.py +22 -12
build/torch213-cxx11-cu126-aarch64-linux/{_megablocks_cuda_18e92fb.abi3.so → _megablocks_cuda_c4d0dc5.abi3.so} +1 -1
build/torch213-cxx11-cu126-aarch64-linux/_ops.py +3 -3
build/torch213-cxx11-cu126-aarch64-linux/metadata.json +5 -5
build/torch213-cxx11-cu126-aarch64-linux/metadata.json.sigstore +1 -1
build/torch213-cxx11-cu126-aarch64-linux/xpu_fused_moe.py +22 -12
build/torch213-cxx11-cu130-aarch64-linux/{_megablocks_cuda_18e92fb.abi3.so → _megablocks_cuda_c4d0dc5.abi3.so} +1 -1
build/torch213-cxx11-cu130-aarch64-linux/_ops.py +3 -3
build/torch213-cxx11-cu130-aarch64-linux/metadata.json +5 -5
build/torch213-cxx11-cu130-aarch64-linux/metadata.json.sigstore +1 -1
build/torch213-cxx11-cu130-aarch64-linux/xpu_fused_moe.py +22 -12
build/torch213-cxx11-cu132-aarch64-linux/{_megablocks_cuda_18e92fb.abi3.so → _megablocks_cuda_c4d0dc5.abi3.so} +1 -1
build/torch213-cxx11-cu132-aarch64-linux/_ops.py +3 -3
build/torch213-cxx11-cu132-aarch64-linux/metadata.json +5 -5
build/torch213-cxx11-cu132-aarch64-linux/metadata.json.sigstore +1 -1
build/torch213-cxx11-cu132-aarch64-linux/xpu_fused_moe.py +22 -12

build/torch211-cxx11-cu126-aarch64-linux/{_megablocks_cuda_18e92fb.abi3.so → _megablocks_cuda_c4d0dc5.abi3.so} RENAMED Viewed

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build/torch211-cxx11-cu126-aarch64-linux/_ops.py CHANGED Viewed

@@ -1,9 +1,9 @@
 import torch
-from . import _megablocks_cuda_18e92fb
-ops = torch.ops._megablocks_cuda_18e92fb
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_megablocks_cuda_18e92fb::{op_name}"

 import torch
+from . import _megablocks_cuda_c4d0dc5
+ops = torch.ops._megablocks_cuda_c4d0dc5
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
+    return f"_megablocks_cuda_c4d0dc5::{op_name}"

build/torch211-cxx11-cu126-aarch64-linux/metadata.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "name": "megablocks",
-  "id": "_megablocks_cuda_18e92fb",
   "version": 1,
   "license": "Apache-2.0",
   "python-depends": [],
@@ -48,8 +48,8 @@
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       "_layers/router.py": "GKC/5//Uf43HT6zXlBTStCMTml2YXn7Jbgq9EgTgDC0=",
       "_layers/sharedexpert_registry.py": "GfwHKjbC7lBXYq9kNifp3HudTdZKSqwsFxGxFLc2krw=",
-      "_megablocks_cuda_18e92fb.abi3.so": "qR6B/Dru6DWeOx+5gcqiEEAaNkzQiyxonABto1gE2uw=",
-      "_ops.py": "baTEjeHRnpTSLod30qZST/UL5GNv7vZoGeNwQWakxbo=",
       "_version.py": "W3l9oUrnmvfBoIy06Lv0y2ezTQhNU04ug/txHcdWJD0=",
       "backend/__init__.py": "4bunAqqjwE93nym8CigHbqVAal7UaYGdONb8lGRfrek=",
       "backend/kernels.py": "2UFNTlmSDk3S5y6UW14MJMPVVsVhmozxQ5wFDxtZHhQ=",
@@ -100,7 +100,7 @@
       "stk/random/__init__.py": "Gr/+Gl20jJ6fJVRfoAUxEcCPeChILQzrzhKVVAWQsRI=",
       "stk/random/random_ops.py": "AluIDj3Vo2ReKAsZz07yydbtndDbJoglekRGIaCoRZE=",
       "stk/random/random_ops_test.py": "B+a4Zb+OPhBtpVA48oc09sBL/AAQO9j/YUFI0nnXxzk=",
-      "xpu_fused_moe.py": "Dp6S3fK/Fc4Sr+Ac94vw2zz0v/QTGp6rYeMIUqE3Y/I="
     }
   },
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       "dirty": false
     },
     "kernel": {
-      "sha": "18e92fb6886201ffb91c173f02593777756c793f",
       "dirty": false
     }
   }

 {
   "name": "megablocks",
+  "id": "_megablocks_cuda_c4d0dc5",
   "version": 1,
   "license": "Apache-2.0",
   "python-depends": [],
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       "_layers/router.py": "GKC/5//Uf43HT6zXlBTStCMTml2YXn7Jbgq9EgTgDC0=",
       "_layers/sharedexpert_registry.py": "GfwHKjbC7lBXYq9kNifp3HudTdZKSqwsFxGxFLc2krw=",
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+      "_ops.py": "9NSxkX5lSS5VhF9+hDazP5NimwZK2V/+nIXR0AQxnHA=",
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       "backend/kernels.py": "2UFNTlmSDk3S5y6UW14MJMPVVsVhmozxQ5wFDxtZHhQ=",
       "stk/random/__init__.py": "Gr/+Gl20jJ6fJVRfoAUxEcCPeChILQzrzhKVVAWQsRI=",
       "stk/random/random_ops.py": "AluIDj3Vo2ReKAsZz07yydbtndDbJoglekRGIaCoRZE=",
       "stk/random/random_ops_test.py": "B+a4Zb+OPhBtpVA48oc09sBL/AAQO9j/YUFI0nnXxzk=",
+      "xpu_fused_moe.py": "sPmuRSpkH1Bmk4hdpz53us8Qpc6yinT9rkld3T8WreI="
     }
   },
   "provenance": {
       "dirty": false
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     "kernel": {
+      "sha": "c4d0dc56661badb9cc47bde647e08fe3b4bf7458",
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build/torch211-cxx11-cu126-aarch64-linux/metadata.json.sigstore CHANGED Viewed

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build/torch211-cxx11-cu126-aarch64-linux/xpu_fused_moe.py CHANGED Viewed

@@ -26,7 +26,7 @@ def _register_xpu_fake_kernels():
     _register_if_available(
         "cutlass_grouped_gemm_interface",
-        lambda ptr_A, ptr_B, ptr_scales, ptr_bias, ptr_D, expert_first_token_offset, N, K, num_experts, is_B_int4, is_B_mxfp4: ptr_D,
     )
     _register_if_available(
@@ -106,12 +106,13 @@ def cutlass_grouped_gemm(input_A, input_B, bias, output, expert_token_count, n,
         K=k,
         num_experts=num_experts,
         is_B_int4=False,
-        is_B_mxfp4=False)
 def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
                              num_rows_per_expert, n, k, num_experts, is_B_int4,
-                             is_B_mxfp4):
     expert_first_token_offset = torch.cat([
         torch.tensor([0],
                      dtype=num_rows_per_expert.dtype,
@@ -129,7 +130,8 @@ def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
         K=k,
         num_experts=num_experts,
         is_B_int4=is_B_int4,
-        is_B_mxfp4=is_B_mxfp4)
 def ceilDiv(a, b):
@@ -206,7 +208,8 @@ def xpu_fused_moe(hidden_states,
                   ep_size=1,
                   is_fp8=False,
                   is_int4=False,
-                  is_mxfp4=False):
     '''
     hidden_states: [num_rows, hidden_size]
     w13: [num_experts, 2*inter_size, hidden_size]
@@ -228,6 +231,7 @@ def xpu_fused_moe(hidden_states,
     num_experts: int
     is_int4: bool
     is_mxfp4: bool
     '''
     # Resolve DTensors to local tensors before passing to custom ops
@@ -266,7 +270,7 @@ def xpu_fused_moe(hidden_states,
     # 4bits support [E, N, K]
     # other types [E, K, N]
-    if not is_int4 and not is_mxfp4:
         if not hasattr(w13, 'xpu_fused_moe'):
             if needs_transpose:
                 w13.data = w13.transpose(-1, -2).contiguous()
@@ -389,7 +393,7 @@ def xpu_fused_moe(hidden_states,
     ########### gemm1 ##################
     input_B = w13
-    if not is_fp8 and not is_int4 and not is_mxfp4:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             ptr_B=input_B,
@@ -401,7 +405,8 @@ def xpu_fused_moe(hidden_states,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
@@ -414,7 +419,8 @@ def xpu_fused_moe(hidden_states,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     # act
     act_output = torch.empty((num_moe_inputs, inter_size),
@@ -435,7 +441,7 @@ def xpu_fused_moe(hidden_states,
     gemm2_output = torch.empty((num_moe_inputs, hidden_size),
                                dtype=hidden_states.dtype,
                                device=hidden_states.device)
-    if not is_fp8 and not is_int4 and not is_mxfp4:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             ptr_B=input_B,
@@ -447,7 +453,8 @@ def xpu_fused_moe(hidden_states,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
@@ -460,7 +467,8 @@ def xpu_fused_moe(hidden_states,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     ops.moe_gather(output, gemm2_output, topk_weights,
                                 permuted_row_to_unpermuted_row,
@@ -633,6 +641,7 @@ class MegaBlocksMoeMLP(torch.nn.Module):
         is_fp8 = getattr(self.experts, "is_fp8", False)
         is_int4 = getattr(self.experts, "is_int4", False)
         is_mxfp4 = getattr(self.experts, "is_mxfp4", False)
         w13_scales = getattr(self.experts, "gate_up_proj_scales", None)
         w2_scales = getattr(self.experts, "down_proj_scales", None)
@@ -685,6 +694,7 @@ class MegaBlocksMoeMLP(torch.nn.Module):
             is_fp8=is_fp8,
             is_int4=is_int4,
             is_mxfp4=is_mxfp4,
         )
         # All-reduce across EP group to combine partial expert outputs

     _register_if_available(
         "cutlass_grouped_gemm_interface",
+        lambda ptr_A, ptr_B, ptr_scales, ptr_bias, ptr_D, expert_first_token_offset, N, K, num_experts, is_B_int4, is_B_mxfp4, is_B_mxfp8=False: ptr_D,
     )
     _register_if_available(
         K=k,
         num_experts=num_experts,
         is_B_int4=False,
+        is_B_mxfp4=False,
+        is_B_mxfp8=False)
 def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
                              num_rows_per_expert, n, k, num_experts, is_B_int4,
+                             is_B_mxfp4, is_B_mxfp8=False):
     expert_first_token_offset = torch.cat([
         torch.tensor([0],
                      dtype=num_rows_per_expert.dtype,
         K=k,
         num_experts=num_experts,
         is_B_int4=is_B_int4,
+        is_B_mxfp4=is_B_mxfp4,
+        is_B_mxfp8=is_B_mxfp8)
 def ceilDiv(a, b):
                   ep_size=1,
                   is_fp8=False,
                   is_int4=False,
+                  is_mxfp4=False,
+                  is_mxfp8=False):
     '''
     hidden_states: [num_rows, hidden_size]
     w13: [num_experts, 2*inter_size, hidden_size]
     num_experts: int
     is_int4: bool
     is_mxfp4: bool
+    is_mxfp8: bool
     '''
     # Resolve DTensors to local tensors before passing to custom ops
     # 4bits support [E, N, K]
     # other types [E, K, N]
+    if not is_int4 and not is_mxfp4 and not is_mxfp8:
         if not hasattr(w13, 'xpu_fused_moe'):
             if needs_transpose:
                 w13.data = w13.transpose(-1, -2).contiguous()
     ########### gemm1 ##################
     input_B = w13
+    if not is_fp8 and not is_int4 and not is_mxfp4 and not is_mxfp8:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             ptr_B=input_B,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     # act
     act_output = torch.empty((num_moe_inputs, inter_size),
     gemm2_output = torch.empty((num_moe_inputs, hidden_size),
                                dtype=hidden_states.dtype,
                                device=hidden_states.device)
+    if not is_fp8 and not is_int4 and not is_mxfp4 and not is_mxfp8:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             ptr_B=input_B,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     ops.moe_gather(output, gemm2_output, topk_weights,
                                 permuted_row_to_unpermuted_row,
         is_fp8 = getattr(self.experts, "is_fp8", False)
         is_int4 = getattr(self.experts, "is_int4", False)
         is_mxfp4 = getattr(self.experts, "is_mxfp4", False)
+        is_mxfp8 = getattr(self.experts, "is_mxfp8", False)
         w13_scales = getattr(self.experts, "gate_up_proj_scales", None)
         w2_scales = getattr(self.experts, "down_proj_scales", None)
             is_fp8=is_fp8,
             is_int4=is_int4,
             is_mxfp4=is_mxfp4,
+            is_mxfp8=is_mxfp8,
         )
         # All-reduce across EP group to combine partial expert outputs

build/torch211-cxx11-cu128-aarch64-linux/{_megablocks_cuda_18e92fb.abi3.so → _megablocks_cuda_c4d0dc5.abi3.so} RENAMED Viewed

@@ -1,3 +1,3 @@
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-oid sha256:866d45eee04fbdfe07ea6174146ce89b3cf23ce3e4b02e6d194b02b120ec6ea5
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build/torch211-cxx11-cu128-aarch64-linux/_ops.py CHANGED Viewed

@@ -1,9 +1,9 @@
 import torch
-from . import _megablocks_cuda_18e92fb
-ops = torch.ops._megablocks_cuda_18e92fb
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_megablocks_cuda_18e92fb::{op_name}"

 import torch
+from . import _megablocks_cuda_c4d0dc5
+ops = torch.ops._megablocks_cuda_c4d0dc5
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
+    return f"_megablocks_cuda_c4d0dc5::{op_name}"

build/torch211-cxx11-cu128-aarch64-linux/metadata.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "name": "megablocks",
-  "id": "_megablocks_cuda_18e92fb",
   "version": 1,
   "license": "Apache-2.0",
   "python-depends": [],
@@ -51,8 +51,8 @@
       "_layers/mpu.py": "BX61/pmqRCVeEMnzpuK8x5NpITBJyZfOawGnDegW1O8=",
       "_layers/router.py": "GKC/5//Uf43HT6zXlBTStCMTml2YXn7Jbgq9EgTgDC0=",
       "_layers/sharedexpert_registry.py": "GfwHKjbC7lBXYq9kNifp3HudTdZKSqwsFxGxFLc2krw=",
-      "_megablocks_cuda_18e92fb.abi3.so": "hm1F7uBPvf4H6mF0FGzomzzyPOPksC5tGUsCsSDsbqU=",
-      "_ops.py": "baTEjeHRnpTSLod30qZST/UL5GNv7vZoGeNwQWakxbo=",
       "_version.py": "W3l9oUrnmvfBoIy06Lv0y2ezTQhNU04ug/txHcdWJD0=",
       "backend/__init__.py": "4bunAqqjwE93nym8CigHbqVAal7UaYGdONb8lGRfrek=",
       "backend/kernels.py": "2UFNTlmSDk3S5y6UW14MJMPVVsVhmozxQ5wFDxtZHhQ=",
@@ -103,7 +103,7 @@
       "stk/random/__init__.py": "Gr/+Gl20jJ6fJVRfoAUxEcCPeChILQzrzhKVVAWQsRI=",
       "stk/random/random_ops.py": "AluIDj3Vo2ReKAsZz07yydbtndDbJoglekRGIaCoRZE=",
       "stk/random/random_ops_test.py": "B+a4Zb+OPhBtpVA48oc09sBL/AAQO9j/YUFI0nnXxzk=",
-      "xpu_fused_moe.py": "Dp6S3fK/Fc4Sr+Ac94vw2zz0v/QTGp6rYeMIUqE3Y/I="
     }
   },
   "provenance": {
@@ -113,7 +113,7 @@
       "dirty": false
     },
     "kernel": {
-      "sha": "18e92fb6886201ffb91c173f02593777756c793f",
       "dirty": false
     }
   }

 {
   "name": "megablocks",
+  "id": "_megablocks_cuda_c4d0dc5",
   "version": 1,
   "license": "Apache-2.0",
   "python-depends": [],
       "_layers/mpu.py": "BX61/pmqRCVeEMnzpuK8x5NpITBJyZfOawGnDegW1O8=",
       "_layers/router.py": "GKC/5//Uf43HT6zXlBTStCMTml2YXn7Jbgq9EgTgDC0=",
       "_layers/sharedexpert_registry.py": "GfwHKjbC7lBXYq9kNifp3HudTdZKSqwsFxGxFLc2krw=",
+      "_megablocks_cuda_c4d0dc5.abi3.so": "pNskomUFQ1HT8AW5RcZvEe0w+O3qv5oRm+EmheG2Ywc=",
+      "_ops.py": "9NSxkX5lSS5VhF9+hDazP5NimwZK2V/+nIXR0AQxnHA=",
       "_version.py": "W3l9oUrnmvfBoIy06Lv0y2ezTQhNU04ug/txHcdWJD0=",
       "backend/__init__.py": "4bunAqqjwE93nym8CigHbqVAal7UaYGdONb8lGRfrek=",
       "backend/kernels.py": "2UFNTlmSDk3S5y6UW14MJMPVVsVhmozxQ5wFDxtZHhQ=",
       "stk/random/__init__.py": "Gr/+Gl20jJ6fJVRfoAUxEcCPeChILQzrzhKVVAWQsRI=",
       "stk/random/random_ops.py": "AluIDj3Vo2ReKAsZz07yydbtndDbJoglekRGIaCoRZE=",
       "stk/random/random_ops_test.py": "B+a4Zb+OPhBtpVA48oc09sBL/AAQO9j/YUFI0nnXxzk=",
+      "xpu_fused_moe.py": "sPmuRSpkH1Bmk4hdpz53us8Qpc6yinT9rkld3T8WreI="
     }
   },
   "provenance": {
       "dirty": false
     },
     "kernel": {
+      "sha": "c4d0dc56661badb9cc47bde647e08fe3b4bf7458",
       "dirty": false
     }
   }

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build/torch211-cxx11-cu128-aarch64-linux/xpu_fused_moe.py CHANGED Viewed

@@ -26,7 +26,7 @@ def _register_xpu_fake_kernels():
     _register_if_available(
         "cutlass_grouped_gemm_interface",
-        lambda ptr_A, ptr_B, ptr_scales, ptr_bias, ptr_D, expert_first_token_offset, N, K, num_experts, is_B_int4, is_B_mxfp4: ptr_D,
     )
     _register_if_available(
@@ -106,12 +106,13 @@ def cutlass_grouped_gemm(input_A, input_B, bias, output, expert_token_count, n,
         K=k,
         num_experts=num_experts,
         is_B_int4=False,
-        is_B_mxfp4=False)
 def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
                              num_rows_per_expert, n, k, num_experts, is_B_int4,
-                             is_B_mxfp4):
     expert_first_token_offset = torch.cat([
         torch.tensor([0],
                      dtype=num_rows_per_expert.dtype,
@@ -129,7 +130,8 @@ def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
         K=k,
         num_experts=num_experts,
         is_B_int4=is_B_int4,
-        is_B_mxfp4=is_B_mxfp4)
 def ceilDiv(a, b):
@@ -206,7 +208,8 @@ def xpu_fused_moe(hidden_states,
                   ep_size=1,
                   is_fp8=False,
                   is_int4=False,
-                  is_mxfp4=False):
     '''
     hidden_states: [num_rows, hidden_size]
     w13: [num_experts, 2*inter_size, hidden_size]
@@ -228,6 +231,7 @@ def xpu_fused_moe(hidden_states,
     num_experts: int
     is_int4: bool
     is_mxfp4: bool
     '''
     # Resolve DTensors to local tensors before passing to custom ops
@@ -266,7 +270,7 @@ def xpu_fused_moe(hidden_states,
     # 4bits support [E, N, K]
     # other types [E, K, N]
-    if not is_int4 and not is_mxfp4:
         if not hasattr(w13, 'xpu_fused_moe'):
             if needs_transpose:
                 w13.data = w13.transpose(-1, -2).contiguous()
@@ -389,7 +393,7 @@ def xpu_fused_moe(hidden_states,
     ########### gemm1 ##################
     input_B = w13
-    if not is_fp8 and not is_int4 and not is_mxfp4:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             ptr_B=input_B,
@@ -401,7 +405,8 @@ def xpu_fused_moe(hidden_states,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
@@ -414,7 +419,8 @@ def xpu_fused_moe(hidden_states,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     # act
     act_output = torch.empty((num_moe_inputs, inter_size),
@@ -435,7 +441,7 @@ def xpu_fused_moe(hidden_states,
     gemm2_output = torch.empty((num_moe_inputs, hidden_size),
                                dtype=hidden_states.dtype,
                                device=hidden_states.device)
-    if not is_fp8 and not is_int4 and not is_mxfp4:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             ptr_B=input_B,
@@ -447,7 +453,8 @@ def xpu_fused_moe(hidden_states,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
@@ -460,7 +467,8 @@ def xpu_fused_moe(hidden_states,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     ops.moe_gather(output, gemm2_output, topk_weights,
                                 permuted_row_to_unpermuted_row,
@@ -633,6 +641,7 @@ class MegaBlocksMoeMLP(torch.nn.Module):
         is_fp8 = getattr(self.experts, "is_fp8", False)
         is_int4 = getattr(self.experts, "is_int4", False)
         is_mxfp4 = getattr(self.experts, "is_mxfp4", False)
         w13_scales = getattr(self.experts, "gate_up_proj_scales", None)
         w2_scales = getattr(self.experts, "down_proj_scales", None)
@@ -685,6 +694,7 @@ class MegaBlocksMoeMLP(torch.nn.Module):
             is_fp8=is_fp8,
             is_int4=is_int4,
             is_mxfp4=is_mxfp4,
         )
         # All-reduce across EP group to combine partial expert outputs

     _register_if_available(
         "cutlass_grouped_gemm_interface",
+        lambda ptr_A, ptr_B, ptr_scales, ptr_bias, ptr_D, expert_first_token_offset, N, K, num_experts, is_B_int4, is_B_mxfp4, is_B_mxfp8=False: ptr_D,
     )
     _register_if_available(
         K=k,
         num_experts=num_experts,
         is_B_int4=False,
+        is_B_mxfp4=False,
+        is_B_mxfp8=False)
 def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
                              num_rows_per_expert, n, k, num_experts, is_B_int4,
+                             is_B_mxfp4, is_B_mxfp8=False):
     expert_first_token_offset = torch.cat([
         torch.tensor([0],
                      dtype=num_rows_per_expert.dtype,
         K=k,
         num_experts=num_experts,
         is_B_int4=is_B_int4,
+        is_B_mxfp4=is_B_mxfp4,
+        is_B_mxfp8=is_B_mxfp8)
 def ceilDiv(a, b):
                   ep_size=1,
                   is_fp8=False,
                   is_int4=False,
+                  is_mxfp4=False,
+                  is_mxfp8=False):
     '''
     hidden_states: [num_rows, hidden_size]
     w13: [num_experts, 2*inter_size, hidden_size]
     num_experts: int
     is_int4: bool
     is_mxfp4: bool
+    is_mxfp8: bool
     '''
     # Resolve DTensors to local tensors before passing to custom ops
     # 4bits support [E, N, K]
     # other types [E, K, N]
+    if not is_int4 and not is_mxfp4 and not is_mxfp8:
         if not hasattr(w13, 'xpu_fused_moe'):
             if needs_transpose:
                 w13.data = w13.transpose(-1, -2).contiguous()
     ########### gemm1 ##################
     input_B = w13
+    if not is_fp8 and not is_int4 and not is_mxfp4 and not is_mxfp8:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             ptr_B=input_B,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     # act
     act_output = torch.empty((num_moe_inputs, inter_size),
     gemm2_output = torch.empty((num_moe_inputs, hidden_size),
                                dtype=hidden_states.dtype,
                                device=hidden_states.device)
+    if not is_fp8 and not is_int4 and not is_mxfp4 and not is_mxfp8:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             ptr_B=input_B,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     ops.moe_gather(output, gemm2_output, topk_weights,
                                 permuted_row_to_unpermuted_row,
         is_fp8 = getattr(self.experts, "is_fp8", False)
         is_int4 = getattr(self.experts, "is_int4", False)
         is_mxfp4 = getattr(self.experts, "is_mxfp4", False)
+        is_mxfp8 = getattr(self.experts, "is_mxfp8", False)
         w13_scales = getattr(self.experts, "gate_up_proj_scales", None)
         w2_scales = getattr(self.experts, "down_proj_scales", None)
             is_fp8=is_fp8,
             is_int4=is_int4,
             is_mxfp4=is_mxfp4,
+            is_mxfp8=is_mxfp8,
         )
         # All-reduce across EP group to combine partial expert outputs

build/torch211-cxx11-cu130-aarch64-linux/{_megablocks_cuda_18e92fb.abi3.so → _megablocks_cuda_c4d0dc5.abi3.so} RENAMED Viewed

@@ -1,3 +1,3 @@
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-oid sha256:06677d7557b216449dc6032f0728807df304fd1454ef2608259e37d39b3bae56
 size 10062568

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+oid sha256:42f42b28a91fe3b5cb4541cba47978da22d283022e01c7520077c4980701bd04
 size 10062568

build/torch211-cxx11-cu130-aarch64-linux/_ops.py CHANGED Viewed

@@ -1,9 +1,9 @@
 import torch
-from . import _megablocks_cuda_18e92fb
-ops = torch.ops._megablocks_cuda_18e92fb
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_megablocks_cuda_18e92fb::{op_name}"

 import torch
+from . import _megablocks_cuda_c4d0dc5
+ops = torch.ops._megablocks_cuda_c4d0dc5
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
+    return f"_megablocks_cuda_c4d0dc5::{op_name}"

build/torch211-cxx11-cu130-aarch64-linux/metadata.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "name": "megablocks",
-  "id": "_megablocks_cuda_18e92fb",
   "version": 1,
   "license": "Apache-2.0",
   "python-depends": [],
@@ -48,8 +48,8 @@
       "_layers/mpu.py": "BX61/pmqRCVeEMnzpuK8x5NpITBJyZfOawGnDegW1O8=",
       "_layers/router.py": "GKC/5//Uf43HT6zXlBTStCMTml2YXn7Jbgq9EgTgDC0=",
       "_layers/sharedexpert_registry.py": "GfwHKjbC7lBXYq9kNifp3HudTdZKSqwsFxGxFLc2krw=",
-      "_megablocks_cuda_18e92fb.abi3.so": "Bmd9dVeyFkSdxgMvByiAffME/RRU7yYIJZ4305s7rlY=",
-      "_ops.py": "baTEjeHRnpTSLod30qZST/UL5GNv7vZoGeNwQWakxbo=",
       "_version.py": "W3l9oUrnmvfBoIy06Lv0y2ezTQhNU04ug/txHcdWJD0=",
       "backend/__init__.py": "4bunAqqjwE93nym8CigHbqVAal7UaYGdONb8lGRfrek=",
       "backend/kernels.py": "2UFNTlmSDk3S5y6UW14MJMPVVsVhmozxQ5wFDxtZHhQ=",
@@ -100,7 +100,7 @@
       "stk/random/__init__.py": "Gr/+Gl20jJ6fJVRfoAUxEcCPeChILQzrzhKVVAWQsRI=",
       "stk/random/random_ops.py": "AluIDj3Vo2ReKAsZz07yydbtndDbJoglekRGIaCoRZE=",
       "stk/random/random_ops_test.py": "B+a4Zb+OPhBtpVA48oc09sBL/AAQO9j/YUFI0nnXxzk=",
-      "xpu_fused_moe.py": "Dp6S3fK/Fc4Sr+Ac94vw2zz0v/QTGp6rYeMIUqE3Y/I="
     }
   },
   "provenance": {
@@ -110,7 +110,7 @@
       "dirty": false
     },
     "kernel": {
-      "sha": "18e92fb6886201ffb91c173f02593777756c793f",
       "dirty": false
     }
   }

 {
   "name": "megablocks",
+  "id": "_megablocks_cuda_c4d0dc5",
   "version": 1,
   "license": "Apache-2.0",
   "python-depends": [],
       "_layers/mpu.py": "BX61/pmqRCVeEMnzpuK8x5NpITBJyZfOawGnDegW1O8=",
       "_layers/router.py": "GKC/5//Uf43HT6zXlBTStCMTml2YXn7Jbgq9EgTgDC0=",
       "_layers/sharedexpert_registry.py": "GfwHKjbC7lBXYq9kNifp3HudTdZKSqwsFxGxFLc2krw=",
+      "_megablocks_cuda_c4d0dc5.abi3.so": "QvQrKKkf47XLRUHLpHl42iLSgwIuAcdSAHfEmAcBvQQ=",
+      "_ops.py": "9NSxkX5lSS5VhF9+hDazP5NimwZK2V/+nIXR0AQxnHA=",
       "_version.py": "W3l9oUrnmvfBoIy06Lv0y2ezTQhNU04ug/txHcdWJD0=",
       "backend/__init__.py": "4bunAqqjwE93nym8CigHbqVAal7UaYGdONb8lGRfrek=",
       "backend/kernels.py": "2UFNTlmSDk3S5y6UW14MJMPVVsVhmozxQ5wFDxtZHhQ=",
       "stk/random/__init__.py": "Gr/+Gl20jJ6fJVRfoAUxEcCPeChILQzrzhKVVAWQsRI=",
       "stk/random/random_ops.py": "AluIDj3Vo2ReKAsZz07yydbtndDbJoglekRGIaCoRZE=",
       "stk/random/random_ops_test.py": "B+a4Zb+OPhBtpVA48oc09sBL/AAQO9j/YUFI0nnXxzk=",
+      "xpu_fused_moe.py": "sPmuRSpkH1Bmk4hdpz53us8Qpc6yinT9rkld3T8WreI="
     }
   },
   "provenance": {
       "dirty": false
     },
     "kernel": {
+      "sha": "c4d0dc56661badb9cc47bde647e08fe3b4bf7458",
       "dirty": false
     }
   }

build/torch211-cxx11-cu130-aarch64-linux/metadata.json.sigstore CHANGED Viewed

@@ -1 +1 @@

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build/torch211-cxx11-cu130-aarch64-linux/xpu_fused_moe.py CHANGED Viewed

@@ -26,7 +26,7 @@ def _register_xpu_fake_kernels():
     _register_if_available(
         "cutlass_grouped_gemm_interface",
-        lambda ptr_A, ptr_B, ptr_scales, ptr_bias, ptr_D, expert_first_token_offset, N, K, num_experts, is_B_int4, is_B_mxfp4: ptr_D,
     )
     _register_if_available(
@@ -106,12 +106,13 @@ def cutlass_grouped_gemm(input_A, input_B, bias, output, expert_token_count, n,
         K=k,
         num_experts=num_experts,
         is_B_int4=False,
-        is_B_mxfp4=False)
 def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
                              num_rows_per_expert, n, k, num_experts, is_B_int4,
-                             is_B_mxfp4):
     expert_first_token_offset = torch.cat([
         torch.tensor([0],
                      dtype=num_rows_per_expert.dtype,
@@ -129,7 +130,8 @@ def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
         K=k,
         num_experts=num_experts,
         is_B_int4=is_B_int4,
-        is_B_mxfp4=is_B_mxfp4)
 def ceilDiv(a, b):
@@ -206,7 +208,8 @@ def xpu_fused_moe(hidden_states,
                   ep_size=1,
                   is_fp8=False,
                   is_int4=False,
-                  is_mxfp4=False):
     '''
     hidden_states: [num_rows, hidden_size]
     w13: [num_experts, 2*inter_size, hidden_size]
@@ -228,6 +231,7 @@ def xpu_fused_moe(hidden_states,
     num_experts: int
     is_int4: bool
     is_mxfp4: bool
     '''
     # Resolve DTensors to local tensors before passing to custom ops
@@ -266,7 +270,7 @@ def xpu_fused_moe(hidden_states,
     # 4bits support [E, N, K]
     # other types [E, K, N]
-    if not is_int4 and not is_mxfp4:
         if not hasattr(w13, 'xpu_fused_moe'):
             if needs_transpose:
                 w13.data = w13.transpose(-1, -2).contiguous()
@@ -389,7 +393,7 @@ def xpu_fused_moe(hidden_states,
     ########### gemm1 ##################
     input_B = w13
-    if not is_fp8 and not is_int4 and not is_mxfp4:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             ptr_B=input_B,
@@ -401,7 +405,8 @@ def xpu_fused_moe(hidden_states,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
@@ -414,7 +419,8 @@ def xpu_fused_moe(hidden_states,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     # act
     act_output = torch.empty((num_moe_inputs, inter_size),
@@ -435,7 +441,7 @@ def xpu_fused_moe(hidden_states,
     gemm2_output = torch.empty((num_moe_inputs, hidden_size),
                                dtype=hidden_states.dtype,
                                device=hidden_states.device)
-    if not is_fp8 and not is_int4 and not is_mxfp4:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             ptr_B=input_B,
@@ -447,7 +453,8 @@ def xpu_fused_moe(hidden_states,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
@@ -460,7 +467,8 @@ def xpu_fused_moe(hidden_states,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     ops.moe_gather(output, gemm2_output, topk_weights,
                                 permuted_row_to_unpermuted_row,
@@ -633,6 +641,7 @@ class MegaBlocksMoeMLP(torch.nn.Module):
         is_fp8 = getattr(self.experts, "is_fp8", False)
         is_int4 = getattr(self.experts, "is_int4", False)
         is_mxfp4 = getattr(self.experts, "is_mxfp4", False)
         w13_scales = getattr(self.experts, "gate_up_proj_scales", None)
         w2_scales = getattr(self.experts, "down_proj_scales", None)
@@ -685,6 +694,7 @@ class MegaBlocksMoeMLP(torch.nn.Module):
             is_fp8=is_fp8,
             is_int4=is_int4,
             is_mxfp4=is_mxfp4,
         )
         # All-reduce across EP group to combine partial expert outputs

     _register_if_available(
         "cutlass_grouped_gemm_interface",
+        lambda ptr_A, ptr_B, ptr_scales, ptr_bias, ptr_D, expert_first_token_offset, N, K, num_experts, is_B_int4, is_B_mxfp4, is_B_mxfp8=False: ptr_D,
     )
     _register_if_available(
         K=k,
         num_experts=num_experts,
         is_B_int4=False,
+        is_B_mxfp4=False,
+        is_B_mxfp8=False)
 def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
                              num_rows_per_expert, n, k, num_experts, is_B_int4,
+                             is_B_mxfp4, is_B_mxfp8=False):
     expert_first_token_offset = torch.cat([
         torch.tensor([0],
                      dtype=num_rows_per_expert.dtype,
         K=k,
         num_experts=num_experts,
         is_B_int4=is_B_int4,
+        is_B_mxfp4=is_B_mxfp4,
+        is_B_mxfp8=is_B_mxfp8)
 def ceilDiv(a, b):
                   ep_size=1,
                   is_fp8=False,
                   is_int4=False,
+                  is_mxfp4=False,
+                  is_mxfp8=False):
     '''
     hidden_states: [num_rows, hidden_size]
     w13: [num_experts, 2*inter_size, hidden_size]
     num_experts: int
     is_int4: bool
     is_mxfp4: bool
+    is_mxfp8: bool
     '''
     # Resolve DTensors to local tensors before passing to custom ops
     # 4bits support [E, N, K]
     # other types [E, K, N]
+    if not is_int4 and not is_mxfp4 and not is_mxfp8:
         if not hasattr(w13, 'xpu_fused_moe'):
             if needs_transpose:
                 w13.data = w13.transpose(-1, -2).contiguous()
     ########### gemm1 ##################
     input_B = w13
+    if not is_fp8 and not is_int4 and not is_mxfp4 and not is_mxfp8:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             ptr_B=input_B,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     # act
     act_output = torch.empty((num_moe_inputs, inter_size),
     gemm2_output = torch.empty((num_moe_inputs, hidden_size),
                                dtype=hidden_states.dtype,
                                device=hidden_states.device)
+    if not is_fp8 and not is_int4 and not is_mxfp4 and not is_mxfp8:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             ptr_B=input_B,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     ops.moe_gather(output, gemm2_output, topk_weights,
                                 permuted_row_to_unpermuted_row,
         is_fp8 = getattr(self.experts, "is_fp8", False)
         is_int4 = getattr(self.experts, "is_int4", False)
         is_mxfp4 = getattr(self.experts, "is_mxfp4", False)
+        is_mxfp8 = getattr(self.experts, "is_mxfp8", False)
         w13_scales = getattr(self.experts, "gate_up_proj_scales", None)
         w2_scales = getattr(self.experts, "down_proj_scales", None)
             is_fp8=is_fp8,
             is_int4=is_int4,
             is_mxfp4=is_mxfp4,
+            is_mxfp8=is_mxfp8,
         )
         # All-reduce across EP group to combine partial expert outputs

build/torch212-cxx11-cu126-aarch64-linux/{_megablocks_cuda_18e92fb.abi3.so → _megablocks_cuda_c4d0dc5.abi3.so} RENAMED Viewed

@@ -1,3 +1,3 @@
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-oid sha256:60ba8c7bf0a8e0e78a110ff50dba38342a3a6a72656c83a6420a0e2964007b7d
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build/torch212-cxx11-cu126-aarch64-linux/_ops.py CHANGED Viewed

@@ -1,9 +1,9 @@
 import torch
-from . import _megablocks_cuda_18e92fb
-ops = torch.ops._megablocks_cuda_18e92fb
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_megablocks_cuda_18e92fb::{op_name}"

 import torch
+from . import _megablocks_cuda_c4d0dc5
+ops = torch.ops._megablocks_cuda_c4d0dc5
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
+    return f"_megablocks_cuda_c4d0dc5::{op_name}"

build/torch212-cxx11-cu126-aarch64-linux/metadata.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "name": "megablocks",
-  "id": "_megablocks_cuda_18e92fb",
   "version": 1,
   "license": "Apache-2.0",
   "python-depends": [],
@@ -48,8 +48,8 @@
       "_layers/mpu.py": "BX61/pmqRCVeEMnzpuK8x5NpITBJyZfOawGnDegW1O8=",
       "_layers/router.py": "GKC/5//Uf43HT6zXlBTStCMTml2YXn7Jbgq9EgTgDC0=",
       "_layers/sharedexpert_registry.py": "GfwHKjbC7lBXYq9kNifp3HudTdZKSqwsFxGxFLc2krw=",
-      "_megablocks_cuda_18e92fb.abi3.so": "YLqMe/Co4OeKEQ/1Dbo4NCo6anJlbIOmQgoOKWQAe30=",
-      "_ops.py": "baTEjeHRnpTSLod30qZST/UL5GNv7vZoGeNwQWakxbo=",
       "_version.py": "W3l9oUrnmvfBoIy06Lv0y2ezTQhNU04ug/txHcdWJD0=",
       "backend/__init__.py": "4bunAqqjwE93nym8CigHbqVAal7UaYGdONb8lGRfrek=",
       "backend/kernels.py": "2UFNTlmSDk3S5y6UW14MJMPVVsVhmozxQ5wFDxtZHhQ=",
@@ -100,7 +100,7 @@
       "stk/random/__init__.py": "Gr/+Gl20jJ6fJVRfoAUxEcCPeChILQzrzhKVVAWQsRI=",
       "stk/random/random_ops.py": "AluIDj3Vo2ReKAsZz07yydbtndDbJoglekRGIaCoRZE=",
       "stk/random/random_ops_test.py": "B+a4Zb+OPhBtpVA48oc09sBL/AAQO9j/YUFI0nnXxzk=",
-      "xpu_fused_moe.py": "Dp6S3fK/Fc4Sr+Ac94vw2zz0v/QTGp6rYeMIUqE3Y/I="
     }
   },
   "provenance": {
@@ -110,7 +110,7 @@
       "dirty": false
     },
     "kernel": {
-      "sha": "18e92fb6886201ffb91c173f02593777756c793f",
       "dirty": false
     }
   }

 {
   "name": "megablocks",
+  "id": "_megablocks_cuda_c4d0dc5",
   "version": 1,
   "license": "Apache-2.0",
   "python-depends": [],
       "_layers/mpu.py": "BX61/pmqRCVeEMnzpuK8x5NpITBJyZfOawGnDegW1O8=",
       "_layers/router.py": "GKC/5//Uf43HT6zXlBTStCMTml2YXn7Jbgq9EgTgDC0=",
       "_layers/sharedexpert_registry.py": "GfwHKjbC7lBXYq9kNifp3HudTdZKSqwsFxGxFLc2krw=",
+      "_megablocks_cuda_c4d0dc5.abi3.so": "t6axbZSLJNEAjiQJr6sTca0acMTl0ebD1VzofU0ZfoU=",
+      "_ops.py": "9NSxkX5lSS5VhF9+hDazP5NimwZK2V/+nIXR0AQxnHA=",
       "_version.py": "W3l9oUrnmvfBoIy06Lv0y2ezTQhNU04ug/txHcdWJD0=",
       "backend/__init__.py": "4bunAqqjwE93nym8CigHbqVAal7UaYGdONb8lGRfrek=",
       "backend/kernels.py": "2UFNTlmSDk3S5y6UW14MJMPVVsVhmozxQ5wFDxtZHhQ=",
       "stk/random/__init__.py": "Gr/+Gl20jJ6fJVRfoAUxEcCPeChILQzrzhKVVAWQsRI=",
       "stk/random/random_ops.py": "AluIDj3Vo2ReKAsZz07yydbtndDbJoglekRGIaCoRZE=",
       "stk/random/random_ops_test.py": "B+a4Zb+OPhBtpVA48oc09sBL/AAQO9j/YUFI0nnXxzk=",
+      "xpu_fused_moe.py": "sPmuRSpkH1Bmk4hdpz53us8Qpc6yinT9rkld3T8WreI="
     }
   },
   "provenance": {
       "dirty": false
     },
     "kernel": {
+      "sha": "c4d0dc56661badb9cc47bde647e08fe3b4bf7458",
       "dirty": false
     }
   }

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build/torch212-cxx11-cu126-aarch64-linux/xpu_fused_moe.py CHANGED Viewed

@@ -26,7 +26,7 @@ def _register_xpu_fake_kernels():
     _register_if_available(
         "cutlass_grouped_gemm_interface",
-        lambda ptr_A, ptr_B, ptr_scales, ptr_bias, ptr_D, expert_first_token_offset, N, K, num_experts, is_B_int4, is_B_mxfp4: ptr_D,
     )
     _register_if_available(
@@ -106,12 +106,13 @@ def cutlass_grouped_gemm(input_A, input_B, bias, output, expert_token_count, n,
         K=k,
         num_experts=num_experts,
         is_B_int4=False,
-        is_B_mxfp4=False)
 def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
                              num_rows_per_expert, n, k, num_experts, is_B_int4,
-                             is_B_mxfp4):
     expert_first_token_offset = torch.cat([
         torch.tensor([0],
                      dtype=num_rows_per_expert.dtype,
@@ -129,7 +130,8 @@ def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
         K=k,
         num_experts=num_experts,
         is_B_int4=is_B_int4,
-        is_B_mxfp4=is_B_mxfp4)
 def ceilDiv(a, b):
@@ -206,7 +208,8 @@ def xpu_fused_moe(hidden_states,
                   ep_size=1,
                   is_fp8=False,
                   is_int4=False,
-                  is_mxfp4=False):
     '''
     hidden_states: [num_rows, hidden_size]
     w13: [num_experts, 2*inter_size, hidden_size]
@@ -228,6 +231,7 @@ def xpu_fused_moe(hidden_states,
     num_experts: int
     is_int4: bool
     is_mxfp4: bool
     '''
     # Resolve DTensors to local tensors before passing to custom ops
@@ -266,7 +270,7 @@ def xpu_fused_moe(hidden_states,
     # 4bits support [E, N, K]
     # other types [E, K, N]
-    if not is_int4 and not is_mxfp4:
         if not hasattr(w13, 'xpu_fused_moe'):
             if needs_transpose:
                 w13.data = w13.transpose(-1, -2).contiguous()
@@ -389,7 +393,7 @@ def xpu_fused_moe(hidden_states,
     ########### gemm1 ##################
     input_B = w13
-    if not is_fp8 and not is_int4 and not is_mxfp4:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             ptr_B=input_B,
@@ -401,7 +405,8 @@ def xpu_fused_moe(hidden_states,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
@@ -414,7 +419,8 @@ def xpu_fused_moe(hidden_states,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     # act
     act_output = torch.empty((num_moe_inputs, inter_size),
@@ -435,7 +441,7 @@ def xpu_fused_moe(hidden_states,
     gemm2_output = torch.empty((num_moe_inputs, hidden_size),
                                dtype=hidden_states.dtype,
                                device=hidden_states.device)
-    if not is_fp8 and not is_int4 and not is_mxfp4:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             ptr_B=input_B,
@@ -447,7 +453,8 @@ def xpu_fused_moe(hidden_states,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
@@ -460,7 +467,8 @@ def xpu_fused_moe(hidden_states,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     ops.moe_gather(output, gemm2_output, topk_weights,
                                 permuted_row_to_unpermuted_row,
@@ -633,6 +641,7 @@ class MegaBlocksMoeMLP(torch.nn.Module):
         is_fp8 = getattr(self.experts, "is_fp8", False)
         is_int4 = getattr(self.experts, "is_int4", False)
         is_mxfp4 = getattr(self.experts, "is_mxfp4", False)
         w13_scales = getattr(self.experts, "gate_up_proj_scales", None)
         w2_scales = getattr(self.experts, "down_proj_scales", None)
@@ -685,6 +694,7 @@ class MegaBlocksMoeMLP(torch.nn.Module):
             is_fp8=is_fp8,
             is_int4=is_int4,
             is_mxfp4=is_mxfp4,
         )
         # All-reduce across EP group to combine partial expert outputs

     _register_if_available(
         "cutlass_grouped_gemm_interface",
+        lambda ptr_A, ptr_B, ptr_scales, ptr_bias, ptr_D, expert_first_token_offset, N, K, num_experts, is_B_int4, is_B_mxfp4, is_B_mxfp8=False: ptr_D,
     )
     _register_if_available(
         K=k,
         num_experts=num_experts,
         is_B_int4=False,
+        is_B_mxfp4=False,
+        is_B_mxfp8=False)
 def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
                              num_rows_per_expert, n, k, num_experts, is_B_int4,
+                             is_B_mxfp4, is_B_mxfp8=False):
     expert_first_token_offset = torch.cat([
         torch.tensor([0],
                      dtype=num_rows_per_expert.dtype,
         K=k,
         num_experts=num_experts,
         is_B_int4=is_B_int4,
+        is_B_mxfp4=is_B_mxfp4,
+        is_B_mxfp8=is_B_mxfp8)
 def ceilDiv(a, b):
                   ep_size=1,
                   is_fp8=False,
                   is_int4=False,
+                  is_mxfp4=False,
+                  is_mxfp8=False):
     '''
     hidden_states: [num_rows, hidden_size]
     w13: [num_experts, 2*inter_size, hidden_size]
     num_experts: int
     is_int4: bool
     is_mxfp4: bool
+    is_mxfp8: bool
     '''
     # Resolve DTensors to local tensors before passing to custom ops
     # 4bits support [E, N, K]
     # other types [E, K, N]
+    if not is_int4 and not is_mxfp4 and not is_mxfp8:
         if not hasattr(w13, 'xpu_fused_moe'):
             if needs_transpose:
                 w13.data = w13.transpose(-1, -2).contiguous()
     ########### gemm1 ##################
     input_B = w13
+    if not is_fp8 and not is_int4 and not is_mxfp4 and not is_mxfp8:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             ptr_B=input_B,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     # act
     act_output = torch.empty((num_moe_inputs, inter_size),
     gemm2_output = torch.empty((num_moe_inputs, hidden_size),
                                dtype=hidden_states.dtype,
                                device=hidden_states.device)
+    if not is_fp8 and not is_int4 and not is_mxfp4 and not is_mxfp8:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             ptr_B=input_B,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     ops.moe_gather(output, gemm2_output, topk_weights,
                                 permuted_row_to_unpermuted_row,
         is_fp8 = getattr(self.experts, "is_fp8", False)
         is_int4 = getattr(self.experts, "is_int4", False)
         is_mxfp4 = getattr(self.experts, "is_mxfp4", False)
+        is_mxfp8 = getattr(self.experts, "is_mxfp8", False)
         w13_scales = getattr(self.experts, "gate_up_proj_scales", None)
         w2_scales = getattr(self.experts, "down_proj_scales", None)
             is_fp8=is_fp8,
             is_int4=is_int4,
             is_mxfp4=is_mxfp4,
+            is_mxfp8=is_mxfp8,
         )
         # All-reduce across EP group to combine partial expert outputs

build/torch212-cxx11-cu130-aarch64-linux/{_megablocks_cuda_18e92fb.abi3.so → _megablocks_cuda_c4d0dc5.abi3.so} RENAMED Viewed

@@ -1,3 +1,3 @@
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-oid sha256:e30ee1d0e47e7ae07083094ca1108d2d72a921b046d22788f311d348c0daf5eb
 size 10062976

 version https://git-lfs.github.com/spec/v1
+oid sha256:2b6dfc16c00e8334974dc4937d458c3cc19003d04e3268beadb01a2b9361ab1e
 size 10062976

build/torch212-cxx11-cu130-aarch64-linux/_ops.py CHANGED Viewed

@@ -1,9 +1,9 @@
 import torch
-from . import _megablocks_cuda_18e92fb
-ops = torch.ops._megablocks_cuda_18e92fb
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_megablocks_cuda_18e92fb::{op_name}"

 import torch
+from . import _megablocks_cuda_c4d0dc5
+ops = torch.ops._megablocks_cuda_c4d0dc5
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
+    return f"_megablocks_cuda_c4d0dc5::{op_name}"

build/torch212-cxx11-cu130-aarch64-linux/metadata.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "name": "megablocks",
-  "id": "_megablocks_cuda_18e92fb",
   "version": 1,
   "license": "Apache-2.0",
   "python-depends": [],
@@ -48,8 +48,8 @@
       "_layers/mpu.py": "BX61/pmqRCVeEMnzpuK8x5NpITBJyZfOawGnDegW1O8=",
       "_layers/router.py": "GKC/5//Uf43HT6zXlBTStCMTml2YXn7Jbgq9EgTgDC0=",
       "_layers/sharedexpert_registry.py": "GfwHKjbC7lBXYq9kNifp3HudTdZKSqwsFxGxFLc2krw=",
-      "_megablocks_cuda_18e92fb.abi3.so": "4w7h0OR+euBwgwlMoRCNLXKpIbBG0ieI8xHTSMDa9es=",
-      "_ops.py": "baTEjeHRnpTSLod30qZST/UL5GNv7vZoGeNwQWakxbo=",
       "_version.py": "W3l9oUrnmvfBoIy06Lv0y2ezTQhNU04ug/txHcdWJD0=",
       "backend/__init__.py": "4bunAqqjwE93nym8CigHbqVAal7UaYGdONb8lGRfrek=",
       "backend/kernels.py": "2UFNTlmSDk3S5y6UW14MJMPVVsVhmozxQ5wFDxtZHhQ=",
@@ -100,7 +100,7 @@
       "stk/random/__init__.py": "Gr/+Gl20jJ6fJVRfoAUxEcCPeChILQzrzhKVVAWQsRI=",
       "stk/random/random_ops.py": "AluIDj3Vo2ReKAsZz07yydbtndDbJoglekRGIaCoRZE=",
       "stk/random/random_ops_test.py": "B+a4Zb+OPhBtpVA48oc09sBL/AAQO9j/YUFI0nnXxzk=",
-      "xpu_fused_moe.py": "Dp6S3fK/Fc4Sr+Ac94vw2zz0v/QTGp6rYeMIUqE3Y/I="
     }
   },
   "provenance": {
@@ -110,7 +110,7 @@
       "dirty": false
     },
     "kernel": {
-      "sha": "18e92fb6886201ffb91c173f02593777756c793f",
       "dirty": false
     }
   }

 {
   "name": "megablocks",
+  "id": "_megablocks_cuda_c4d0dc5",
   "version": 1,
   "license": "Apache-2.0",
   "python-depends": [],
       "_layers/mpu.py": "BX61/pmqRCVeEMnzpuK8x5NpITBJyZfOawGnDegW1O8=",
       "_layers/router.py": "GKC/5//Uf43HT6zXlBTStCMTml2YXn7Jbgq9EgTgDC0=",
       "_layers/sharedexpert_registry.py": "GfwHKjbC7lBXYq9kNifp3HudTdZKSqwsFxGxFLc2krw=",
+      "_megablocks_cuda_c4d0dc5.abi3.so": "K238FsAOgzSXTcSTfUWMPMGQA9BOMmi+rbAaK5Nhqx4=",
+      "_ops.py": "9NSxkX5lSS5VhF9+hDazP5NimwZK2V/+nIXR0AQxnHA=",
       "_version.py": "W3l9oUrnmvfBoIy06Lv0y2ezTQhNU04ug/txHcdWJD0=",
       "backend/__init__.py": "4bunAqqjwE93nym8CigHbqVAal7UaYGdONb8lGRfrek=",
       "backend/kernels.py": "2UFNTlmSDk3S5y6UW14MJMPVVsVhmozxQ5wFDxtZHhQ=",
       "stk/random/__init__.py": "Gr/+Gl20jJ6fJVRfoAUxEcCPeChILQzrzhKVVAWQsRI=",
       "stk/random/random_ops.py": "AluIDj3Vo2ReKAsZz07yydbtndDbJoglekRGIaCoRZE=",
       "stk/random/random_ops_test.py": "B+a4Zb+OPhBtpVA48oc09sBL/AAQO9j/YUFI0nnXxzk=",
+      "xpu_fused_moe.py": "sPmuRSpkH1Bmk4hdpz53us8Qpc6yinT9rkld3T8WreI="
     }
   },
   "provenance": {
       "dirty": false
     },
     "kernel": {
+      "sha": "c4d0dc56661badb9cc47bde647e08fe3b4bf7458",
       "dirty": false
     }
   }

build/torch212-cxx11-cu130-aarch64-linux/metadata.json.sigstore CHANGED Viewed

@@ -1 +1 @@

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build/torch212-cxx11-cu130-aarch64-linux/xpu_fused_moe.py CHANGED Viewed

@@ -26,7 +26,7 @@ def _register_xpu_fake_kernels():
     _register_if_available(
         "cutlass_grouped_gemm_interface",
-        lambda ptr_A, ptr_B, ptr_scales, ptr_bias, ptr_D, expert_first_token_offset, N, K, num_experts, is_B_int4, is_B_mxfp4: ptr_D,
     )
     _register_if_available(
@@ -106,12 +106,13 @@ def cutlass_grouped_gemm(input_A, input_B, bias, output, expert_token_count, n,
         K=k,
         num_experts=num_experts,
         is_B_int4=False,
-        is_B_mxfp4=False)
 def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
                              num_rows_per_expert, n, k, num_experts, is_B_int4,
-                             is_B_mxfp4):
     expert_first_token_offset = torch.cat([
         torch.tensor([0],
                      dtype=num_rows_per_expert.dtype,
@@ -129,7 +130,8 @@ def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
         K=k,
         num_experts=num_experts,
         is_B_int4=is_B_int4,
-        is_B_mxfp4=is_B_mxfp4)
 def ceilDiv(a, b):
@@ -206,7 +208,8 @@ def xpu_fused_moe(hidden_states,
                   ep_size=1,
                   is_fp8=False,
                   is_int4=False,
-                  is_mxfp4=False):
     '''
     hidden_states: [num_rows, hidden_size]
     w13: [num_experts, 2*inter_size, hidden_size]
@@ -228,6 +231,7 @@ def xpu_fused_moe(hidden_states,
     num_experts: int
     is_int4: bool
     is_mxfp4: bool
     '''
     # Resolve DTensors to local tensors before passing to custom ops
@@ -266,7 +270,7 @@ def xpu_fused_moe(hidden_states,
     # 4bits support [E, N, K]
     # other types [E, K, N]
-    if not is_int4 and not is_mxfp4:
         if not hasattr(w13, 'xpu_fused_moe'):
             if needs_transpose:
                 w13.data = w13.transpose(-1, -2).contiguous()
@@ -389,7 +393,7 @@ def xpu_fused_moe(hidden_states,
     ########### gemm1 ##################
     input_B = w13
-    if not is_fp8 and not is_int4 and not is_mxfp4:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             ptr_B=input_B,
@@ -401,7 +405,8 @@ def xpu_fused_moe(hidden_states,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
@@ -414,7 +419,8 @@ def xpu_fused_moe(hidden_states,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     # act
     act_output = torch.empty((num_moe_inputs, inter_size),
@@ -435,7 +441,7 @@ def xpu_fused_moe(hidden_states,
     gemm2_output = torch.empty((num_moe_inputs, hidden_size),
                                dtype=hidden_states.dtype,
                                device=hidden_states.device)
-    if not is_fp8 and not is_int4 and not is_mxfp4:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             ptr_B=input_B,
@@ -447,7 +453,8 @@ def xpu_fused_moe(hidden_states,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
@@ -460,7 +467,8 @@ def xpu_fused_moe(hidden_states,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     ops.moe_gather(output, gemm2_output, topk_weights,
                                 permuted_row_to_unpermuted_row,
@@ -633,6 +641,7 @@ class MegaBlocksMoeMLP(torch.nn.Module):
         is_fp8 = getattr(self.experts, "is_fp8", False)
         is_int4 = getattr(self.experts, "is_int4", False)
         is_mxfp4 = getattr(self.experts, "is_mxfp4", False)
         w13_scales = getattr(self.experts, "gate_up_proj_scales", None)
         w2_scales = getattr(self.experts, "down_proj_scales", None)
@@ -685,6 +694,7 @@ class MegaBlocksMoeMLP(torch.nn.Module):
             is_fp8=is_fp8,
             is_int4=is_int4,
             is_mxfp4=is_mxfp4,
         )
         # All-reduce across EP group to combine partial expert outputs

     _register_if_available(
         "cutlass_grouped_gemm_interface",
+        lambda ptr_A, ptr_B, ptr_scales, ptr_bias, ptr_D, expert_first_token_offset, N, K, num_experts, is_B_int4, is_B_mxfp4, is_B_mxfp8=False: ptr_D,
     )
     _register_if_available(
         K=k,
         num_experts=num_experts,
         is_B_int4=False,
+        is_B_mxfp4=False,
+        is_B_mxfp8=False)
 def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
                              num_rows_per_expert, n, k, num_experts, is_B_int4,
+                             is_B_mxfp4, is_B_mxfp8=False):
     expert_first_token_offset = torch.cat([
         torch.tensor([0],
                      dtype=num_rows_per_expert.dtype,
         K=k,
         num_experts=num_experts,
         is_B_int4=is_B_int4,
+        is_B_mxfp4=is_B_mxfp4,
+        is_B_mxfp8=is_B_mxfp8)
 def ceilDiv(a, b):
                   ep_size=1,
                   is_fp8=False,
                   is_int4=False,
+                  is_mxfp4=False,
+                  is_mxfp8=False):
     '''
     hidden_states: [num_rows, hidden_size]
     w13: [num_experts, 2*inter_size, hidden_size]
     num_experts: int
     is_int4: bool
     is_mxfp4: bool
+    is_mxfp8: bool
     '''
     # Resolve DTensors to local tensors before passing to custom ops
     # 4bits support [E, N, K]
     # other types [E, K, N]
+    if not is_int4 and not is_mxfp4 and not is_mxfp8:
         if not hasattr(w13, 'xpu_fused_moe'):
             if needs_transpose:
                 w13.data = w13.transpose(-1, -2).contiguous()
     ########### gemm1 ##################
     input_B = w13
+    if not is_fp8 and not is_int4 and not is_mxfp4 and not is_mxfp8:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             ptr_B=input_B,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     # act
     act_output = torch.empty((num_moe_inputs, inter_size),
     gemm2_output = torch.empty((num_moe_inputs, hidden_size),
                                dtype=hidden_states.dtype,
                                device=hidden_states.device)
+    if not is_fp8 and not is_int4 and not is_mxfp4 and not is_mxfp8:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             ptr_B=input_B,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     ops.moe_gather(output, gemm2_output, topk_weights,
                                 permuted_row_to_unpermuted_row,
         is_fp8 = getattr(self.experts, "is_fp8", False)
         is_int4 = getattr(self.experts, "is_int4", False)
         is_mxfp4 = getattr(self.experts, "is_mxfp4", False)
+        is_mxfp8 = getattr(self.experts, "is_mxfp8", False)
         w13_scales = getattr(self.experts, "gate_up_proj_scales", None)
         w2_scales = getattr(self.experts, "down_proj_scales", None)
             is_fp8=is_fp8,
             is_int4=is_int4,
             is_mxfp4=is_mxfp4,
+            is_mxfp8=is_mxfp8,
         )
         # All-reduce across EP group to combine partial expert outputs

build/torch212-cxx11-cu132-aarch64-linux/{_megablocks_cuda_18e92fb.abi3.so → _megablocks_cuda_c4d0dc5.abi3.so} RENAMED Viewed

@@ -1,3 +1,3 @@
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-oid sha256:3bc514ea255b28d4ebe4ecdc475045cda997dfb48c73eb2f070f20723260180d
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build/torch212-cxx11-cu132-aarch64-linux/_ops.py CHANGED Viewed

@@ -1,9 +1,9 @@
 import torch
-from . import _megablocks_cuda_18e92fb
-ops = torch.ops._megablocks_cuda_18e92fb
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_megablocks_cuda_18e92fb::{op_name}"

 import torch
+from . import _megablocks_cuda_c4d0dc5
+ops = torch.ops._megablocks_cuda_c4d0dc5
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
+    return f"_megablocks_cuda_c4d0dc5::{op_name}"

build/torch212-cxx11-cu132-aarch64-linux/metadata.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "name": "megablocks",
-  "id": "_megablocks_cuda_18e92fb",
   "version": 1,
   "license": "Apache-2.0",
   "python-depends": [],
@@ -48,8 +48,8 @@
       "_layers/mpu.py": "BX61/pmqRCVeEMnzpuK8x5NpITBJyZfOawGnDegW1O8=",
       "_layers/router.py": "GKC/5//Uf43HT6zXlBTStCMTml2YXn7Jbgq9EgTgDC0=",
       "_layers/sharedexpert_registry.py": "GfwHKjbC7lBXYq9kNifp3HudTdZKSqwsFxGxFLc2krw=",
-      "_megablocks_cuda_18e92fb.abi3.so": "O8UU6iVbKNTr5OzcR1BFzamX37SMc+svBw8gcjJgGA0=",
-      "_ops.py": "baTEjeHRnpTSLod30qZST/UL5GNv7vZoGeNwQWakxbo=",
       "_version.py": "W3l9oUrnmvfBoIy06Lv0y2ezTQhNU04ug/txHcdWJD0=",
       "backend/__init__.py": "4bunAqqjwE93nym8CigHbqVAal7UaYGdONb8lGRfrek=",
       "backend/kernels.py": "2UFNTlmSDk3S5y6UW14MJMPVVsVhmozxQ5wFDxtZHhQ=",
@@ -100,7 +100,7 @@
       "stk/random/__init__.py": "Gr/+Gl20jJ6fJVRfoAUxEcCPeChILQzrzhKVVAWQsRI=",
       "stk/random/random_ops.py": "AluIDj3Vo2ReKAsZz07yydbtndDbJoglekRGIaCoRZE=",
       "stk/random/random_ops_test.py": "B+a4Zb+OPhBtpVA48oc09sBL/AAQO9j/YUFI0nnXxzk=",
-      "xpu_fused_moe.py": "Dp6S3fK/Fc4Sr+Ac94vw2zz0v/QTGp6rYeMIUqE3Y/I="
     }
   },
   "provenance": {
@@ -110,7 +110,7 @@
       "dirty": false
     },
     "kernel": {
-      "sha": "18e92fb6886201ffb91c173f02593777756c793f",
       "dirty": false
     }
   }

 {
   "name": "megablocks",
+  "id": "_megablocks_cuda_c4d0dc5",
   "version": 1,
   "license": "Apache-2.0",
   "python-depends": [],
       "_layers/mpu.py": "BX61/pmqRCVeEMnzpuK8x5NpITBJyZfOawGnDegW1O8=",
       "_layers/router.py": "GKC/5//Uf43HT6zXlBTStCMTml2YXn7Jbgq9EgTgDC0=",
       "_layers/sharedexpert_registry.py": "GfwHKjbC7lBXYq9kNifp3HudTdZKSqwsFxGxFLc2krw=",
+      "_megablocks_cuda_c4d0dc5.abi3.so": "B59vboiTqc5aL6F/1xekgoKpMIYH3y9lrXFNf5suqa0=",
+      "_ops.py": "9NSxkX5lSS5VhF9+hDazP5NimwZK2V/+nIXR0AQxnHA=",
       "_version.py": "W3l9oUrnmvfBoIy06Lv0y2ezTQhNU04ug/txHcdWJD0=",
       "backend/__init__.py": "4bunAqqjwE93nym8CigHbqVAal7UaYGdONb8lGRfrek=",
       "backend/kernels.py": "2UFNTlmSDk3S5y6UW14MJMPVVsVhmozxQ5wFDxtZHhQ=",
       "stk/random/__init__.py": "Gr/+Gl20jJ6fJVRfoAUxEcCPeChILQzrzhKVVAWQsRI=",
       "stk/random/random_ops.py": "AluIDj3Vo2ReKAsZz07yydbtndDbJoglekRGIaCoRZE=",
       "stk/random/random_ops_test.py": "B+a4Zb+OPhBtpVA48oc09sBL/AAQO9j/YUFI0nnXxzk=",
+      "xpu_fused_moe.py": "sPmuRSpkH1Bmk4hdpz53us8Qpc6yinT9rkld3T8WreI="
     }
   },
   "provenance": {
       "dirty": false
     },
     "kernel": {
+      "sha": "c4d0dc56661badb9cc47bde647e08fe3b4bf7458",
       "dirty": false
     }
   }

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build/torch212-cxx11-cu132-aarch64-linux/xpu_fused_moe.py CHANGED Viewed

@@ -26,7 +26,7 @@ def _register_xpu_fake_kernels():
     _register_if_available(
         "cutlass_grouped_gemm_interface",
-        lambda ptr_A, ptr_B, ptr_scales, ptr_bias, ptr_D, expert_first_token_offset, N, K, num_experts, is_B_int4, is_B_mxfp4: ptr_D,
     )
     _register_if_available(
@@ -106,12 +106,13 @@ def cutlass_grouped_gemm(input_A, input_B, bias, output, expert_token_count, n,
         K=k,
         num_experts=num_experts,
         is_B_int4=False,
-        is_B_mxfp4=False)
 def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
                              num_rows_per_expert, n, k, num_experts, is_B_int4,
-                             is_B_mxfp4):
     expert_first_token_offset = torch.cat([
         torch.tensor([0],
                      dtype=num_rows_per_expert.dtype,
@@ -129,7 +130,8 @@ def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
         K=k,
         num_experts=num_experts,
         is_B_int4=is_B_int4,
-        is_B_mxfp4=is_B_mxfp4)
 def ceilDiv(a, b):
@@ -206,7 +208,8 @@ def xpu_fused_moe(hidden_states,
                   ep_size=1,
                   is_fp8=False,
                   is_int4=False,
-                  is_mxfp4=False):
     '''
     hidden_states: [num_rows, hidden_size]
     w13: [num_experts, 2*inter_size, hidden_size]
@@ -228,6 +231,7 @@ def xpu_fused_moe(hidden_states,
     num_experts: int
     is_int4: bool
     is_mxfp4: bool
     '''
     # Resolve DTensors to local tensors before passing to custom ops
@@ -266,7 +270,7 @@ def xpu_fused_moe(hidden_states,
     # 4bits support [E, N, K]
     # other types [E, K, N]
-    if not is_int4 and not is_mxfp4:
         if not hasattr(w13, 'xpu_fused_moe'):
             if needs_transpose:
                 w13.data = w13.transpose(-1, -2).contiguous()
@@ -389,7 +393,7 @@ def xpu_fused_moe(hidden_states,
     ########### gemm1 ##################
     input_B = w13
-    if not is_fp8 and not is_int4 and not is_mxfp4:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             ptr_B=input_B,
@@ -401,7 +405,8 @@ def xpu_fused_moe(hidden_states,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
@@ -414,7 +419,8 @@ def xpu_fused_moe(hidden_states,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     # act
     act_output = torch.empty((num_moe_inputs, inter_size),
@@ -435,7 +441,7 @@ def xpu_fused_moe(hidden_states,
     gemm2_output = torch.empty((num_moe_inputs, hidden_size),
                                dtype=hidden_states.dtype,
                                device=hidden_states.device)
-    if not is_fp8 and not is_int4 and not is_mxfp4:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             ptr_B=input_B,
@@ -447,7 +453,8 @@ def xpu_fused_moe(hidden_states,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
@@ -460,7 +467,8 @@ def xpu_fused_moe(hidden_states,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     ops.moe_gather(output, gemm2_output, topk_weights,
                                 permuted_row_to_unpermuted_row,
@@ -633,6 +641,7 @@ class MegaBlocksMoeMLP(torch.nn.Module):
         is_fp8 = getattr(self.experts, "is_fp8", False)
         is_int4 = getattr(self.experts, "is_int4", False)
         is_mxfp4 = getattr(self.experts, "is_mxfp4", False)
         w13_scales = getattr(self.experts, "gate_up_proj_scales", None)
         w2_scales = getattr(self.experts, "down_proj_scales", None)
@@ -685,6 +694,7 @@ class MegaBlocksMoeMLP(torch.nn.Module):
             is_fp8=is_fp8,
             is_int4=is_int4,
             is_mxfp4=is_mxfp4,
         )
         # All-reduce across EP group to combine partial expert outputs

     _register_if_available(
         "cutlass_grouped_gemm_interface",
+        lambda ptr_A, ptr_B, ptr_scales, ptr_bias, ptr_D, expert_first_token_offset, N, K, num_experts, is_B_int4, is_B_mxfp4, is_B_mxfp8=False: ptr_D,
     )
     _register_if_available(
         K=k,
         num_experts=num_experts,
         is_B_int4=False,
+        is_B_mxfp4=False,
+        is_B_mxfp8=False)
 def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
                              num_rows_per_expert, n, k, num_experts, is_B_int4,
+                             is_B_mxfp4, is_B_mxfp8=False):
     expert_first_token_offset = torch.cat([
         torch.tensor([0],
                      dtype=num_rows_per_expert.dtype,
         K=k,
         num_experts=num_experts,
         is_B_int4=is_B_int4,
+        is_B_mxfp4=is_B_mxfp4,
+        is_B_mxfp8=is_B_mxfp8)
 def ceilDiv(a, b):
                   ep_size=1,
                   is_fp8=False,
                   is_int4=False,
+                  is_mxfp4=False,
+                  is_mxfp8=False):
     '''
     hidden_states: [num_rows, hidden_size]
     w13: [num_experts, 2*inter_size, hidden_size]
     num_experts: int
     is_int4: bool
     is_mxfp4: bool
+    is_mxfp8: bool
     '''
     # Resolve DTensors to local tensors before passing to custom ops
     # 4bits support [E, N, K]
     # other types [E, K, N]
+    if not is_int4 and not is_mxfp4 and not is_mxfp8:
         if not hasattr(w13, 'xpu_fused_moe'):
             if needs_transpose:
                 w13.data = w13.transpose(-1, -2).contiguous()
     ########### gemm1 ##################
     input_B = w13
+    if not is_fp8 and not is_int4 and not is_mxfp4 and not is_mxfp8:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             ptr_B=input_B,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     # act
     act_output = torch.empty((num_moe_inputs, inter_size),
     gemm2_output = torch.empty((num_moe_inputs, hidden_size),
                                dtype=hidden_states.dtype,
                                device=hidden_states.device)
+    if not is_fp8 and not is_int4 and not is_mxfp4 and not is_mxfp8:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             ptr_B=input_B,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     ops.moe_gather(output, gemm2_output, topk_weights,
                                 permuted_row_to_unpermuted_row,
         is_fp8 = getattr(self.experts, "is_fp8", False)
         is_int4 = getattr(self.experts, "is_int4", False)
         is_mxfp4 = getattr(self.experts, "is_mxfp4", False)
+        is_mxfp8 = getattr(self.experts, "is_mxfp8", False)
         w13_scales = getattr(self.experts, "gate_up_proj_scales", None)
         w2_scales = getattr(self.experts, "down_proj_scales", None)
             is_fp8=is_fp8,
             is_int4=is_int4,
             is_mxfp4=is_mxfp4,
+            is_mxfp8=is_mxfp8,
         )
         # All-reduce across EP group to combine partial expert outputs

build/torch213-cxx11-cu126-aarch64-linux/{_megablocks_cuda_18e92fb.abi3.so → _megablocks_cuda_c4d0dc5.abi3.so} RENAMED Viewed

@@ -1,3 +1,3 @@
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+oid sha256:158335ce098593106590acae2ece601bca6f9f3350e61a5c150e58fbaa62ce41
 size 13179656

build/torch213-cxx11-cu126-aarch64-linux/_ops.py CHANGED Viewed

@@ -1,9 +1,9 @@
 import torch
-from . import _megablocks_cuda_18e92fb
-ops = torch.ops._megablocks_cuda_18e92fb
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_megablocks_cuda_18e92fb::{op_name}"

 import torch
+from . import _megablocks_cuda_c4d0dc5
+ops = torch.ops._megablocks_cuda_c4d0dc5
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
+    return f"_megablocks_cuda_c4d0dc5::{op_name}"

build/torch213-cxx11-cu126-aarch64-linux/metadata.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "name": "megablocks",
-  "id": "_megablocks_cuda_18e92fb",
   "version": 1,
   "license": "Apache-2.0",
   "python-depends": [],
@@ -48,8 +48,8 @@
       "_layers/mpu.py": "BX61/pmqRCVeEMnzpuK8x5NpITBJyZfOawGnDegW1O8=",
       "_layers/router.py": "GKC/5//Uf43HT6zXlBTStCMTml2YXn7Jbgq9EgTgDC0=",
       "_layers/sharedexpert_registry.py": "GfwHKjbC7lBXYq9kNifp3HudTdZKSqwsFxGxFLc2krw=",
-      "_megablocks_cuda_18e92fb.abi3.so": "0zawyRyTR65o/9g4QVEDrfZuI9Vcg8WgqgNW/rS96BE=",
-      "_ops.py": "baTEjeHRnpTSLod30qZST/UL5GNv7vZoGeNwQWakxbo=",
       "_version.py": "W3l9oUrnmvfBoIy06Lv0y2ezTQhNU04ug/txHcdWJD0=",
       "backend/__init__.py": "4bunAqqjwE93nym8CigHbqVAal7UaYGdONb8lGRfrek=",
       "backend/kernels.py": "2UFNTlmSDk3S5y6UW14MJMPVVsVhmozxQ5wFDxtZHhQ=",
@@ -100,7 +100,7 @@
       "stk/random/__init__.py": "Gr/+Gl20jJ6fJVRfoAUxEcCPeChILQzrzhKVVAWQsRI=",
       "stk/random/random_ops.py": "AluIDj3Vo2ReKAsZz07yydbtndDbJoglekRGIaCoRZE=",
       "stk/random/random_ops_test.py": "B+a4Zb+OPhBtpVA48oc09sBL/AAQO9j/YUFI0nnXxzk=",
-      "xpu_fused_moe.py": "Dp6S3fK/Fc4Sr+Ac94vw2zz0v/QTGp6rYeMIUqE3Y/I="
     }
   },
   "provenance": {
@@ -110,7 +110,7 @@
       "dirty": false
     },
     "kernel": {
-      "sha": "18e92fb6886201ffb91c173f02593777756c793f",
       "dirty": false
     }
   }

 {
   "name": "megablocks",
+  "id": "_megablocks_cuda_c4d0dc5",
   "version": 1,
   "license": "Apache-2.0",
   "python-depends": [],
       "_layers/mpu.py": "BX61/pmqRCVeEMnzpuK8x5NpITBJyZfOawGnDegW1O8=",
       "_layers/router.py": "GKC/5//Uf43HT6zXlBTStCMTml2YXn7Jbgq9EgTgDC0=",
       "_layers/sharedexpert_registry.py": "GfwHKjbC7lBXYq9kNifp3HudTdZKSqwsFxGxFLc2krw=",
+      "_megablocks_cuda_c4d0dc5.abi3.so": "FYM1zgmFkxBlkKyuLs5gG8pvnzNQ5hpcFQ5Y+6pizkE=",
+      "_ops.py": "9NSxkX5lSS5VhF9+hDazP5NimwZK2V/+nIXR0AQxnHA=",
       "_version.py": "W3l9oUrnmvfBoIy06Lv0y2ezTQhNU04ug/txHcdWJD0=",
       "backend/__init__.py": "4bunAqqjwE93nym8CigHbqVAal7UaYGdONb8lGRfrek=",
       "backend/kernels.py": "2UFNTlmSDk3S5y6UW14MJMPVVsVhmozxQ5wFDxtZHhQ=",
       "stk/random/__init__.py": "Gr/+Gl20jJ6fJVRfoAUxEcCPeChILQzrzhKVVAWQsRI=",
       "stk/random/random_ops.py": "AluIDj3Vo2ReKAsZz07yydbtndDbJoglekRGIaCoRZE=",
       "stk/random/random_ops_test.py": "B+a4Zb+OPhBtpVA48oc09sBL/AAQO9j/YUFI0nnXxzk=",
+      "xpu_fused_moe.py": "sPmuRSpkH1Bmk4hdpz53us8Qpc6yinT9rkld3T8WreI="
     }
   },
   "provenance": {
       "dirty": false
     },
     "kernel": {
+      "sha": "c4d0dc56661badb9cc47bde647e08fe3b4bf7458",
       "dirty": false
     }
   }

build/torch213-cxx11-cu126-aarch64-linux/metadata.json.sigstore CHANGED Viewed

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kJZazB6U2tGSVZGUnFaRzF2YlZOdlRGUkNRVm81TW5RMVl6ZHdPRFZCVWxCbk56SXpOMmRMZEZrMWFucENZbTFYVUhaNlVUMDlDaTB0TFMwdFJVNUVJRU5GVWxSSlJrbERRVlJGTFMwdExTMEsifX19fQ=="}],"timestampVerificationData":{"rfc3161Timestamps":[{"signedTimestamp":"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"}]}},"messageSignature":{"messageDigest":{"algorithm":"SHA2_256","digest":"CgQGjFHrnwFzCmC/6YFeT43lkrnvZQDXXBrGaVUsXMo="},"signature":"MEQCIAWpdGpEQX0pdwDUqZ+bY97ccWbbM+/pr/7TegjoXZzOAiAhgvARpNS+H7cJK2Q6jT5D5D4KaQbFOKbWvs5hLa+wpw=="}}

build/torch213-cxx11-cu126-aarch64-linux/xpu_fused_moe.py CHANGED Viewed

@@ -26,7 +26,7 @@ def _register_xpu_fake_kernels():
     _register_if_available(
         "cutlass_grouped_gemm_interface",
-        lambda ptr_A, ptr_B, ptr_scales, ptr_bias, ptr_D, expert_first_token_offset, N, K, num_experts, is_B_int4, is_B_mxfp4: ptr_D,
     )
     _register_if_available(
@@ -106,12 +106,13 @@ def cutlass_grouped_gemm(input_A, input_B, bias, output, expert_token_count, n,
         K=k,
         num_experts=num_experts,
         is_B_int4=False,
-        is_B_mxfp4=False)
 def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
                              num_rows_per_expert, n, k, num_experts, is_B_int4,
-                             is_B_mxfp4):
     expert_first_token_offset = torch.cat([
         torch.tensor([0],
                      dtype=num_rows_per_expert.dtype,
@@ -129,7 +130,8 @@ def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
         K=k,
         num_experts=num_experts,
         is_B_int4=is_B_int4,
-        is_B_mxfp4=is_B_mxfp4)
 def ceilDiv(a, b):
@@ -206,7 +208,8 @@ def xpu_fused_moe(hidden_states,
                   ep_size=1,
                   is_fp8=False,
                   is_int4=False,
-                  is_mxfp4=False):
     '''
     hidden_states: [num_rows, hidden_size]
     w13: [num_experts, 2*inter_size, hidden_size]
@@ -228,6 +231,7 @@ def xpu_fused_moe(hidden_states,
     num_experts: int
     is_int4: bool
     is_mxfp4: bool
     '''
     # Resolve DTensors to local tensors before passing to custom ops
@@ -266,7 +270,7 @@ def xpu_fused_moe(hidden_states,
     # 4bits support [E, N, K]
     # other types [E, K, N]
-    if not is_int4 and not is_mxfp4:
         if not hasattr(w13, 'xpu_fused_moe'):
             if needs_transpose:
                 w13.data = w13.transpose(-1, -2).contiguous()
@@ -389,7 +393,7 @@ def xpu_fused_moe(hidden_states,
     ########### gemm1 ##################
     input_B = w13
-    if not is_fp8 and not is_int4 and not is_mxfp4:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             ptr_B=input_B,
@@ -401,7 +405,8 @@ def xpu_fused_moe(hidden_states,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
@@ -414,7 +419,8 @@ def xpu_fused_moe(hidden_states,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     # act
     act_output = torch.empty((num_moe_inputs, inter_size),
@@ -435,7 +441,7 @@ def xpu_fused_moe(hidden_states,
     gemm2_output = torch.empty((num_moe_inputs, hidden_size),
                                dtype=hidden_states.dtype,
                                device=hidden_states.device)
-    if not is_fp8 and not is_int4 and not is_mxfp4:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             ptr_B=input_B,
@@ -447,7 +453,8 @@ def xpu_fused_moe(hidden_states,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
@@ -460,7 +467,8 @@ def xpu_fused_moe(hidden_states,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     ops.moe_gather(output, gemm2_output, topk_weights,
                                 permuted_row_to_unpermuted_row,
@@ -633,6 +641,7 @@ class MegaBlocksMoeMLP(torch.nn.Module):
         is_fp8 = getattr(self.experts, "is_fp8", False)
         is_int4 = getattr(self.experts, "is_int4", False)
         is_mxfp4 = getattr(self.experts, "is_mxfp4", False)
         w13_scales = getattr(self.experts, "gate_up_proj_scales", None)
         w2_scales = getattr(self.experts, "down_proj_scales", None)
@@ -685,6 +694,7 @@ class MegaBlocksMoeMLP(torch.nn.Module):
             is_fp8=is_fp8,
             is_int4=is_int4,
             is_mxfp4=is_mxfp4,
         )
         # All-reduce across EP group to combine partial expert outputs

     _register_if_available(
         "cutlass_grouped_gemm_interface",
+        lambda ptr_A, ptr_B, ptr_scales, ptr_bias, ptr_D, expert_first_token_offset, N, K, num_experts, is_B_int4, is_B_mxfp4, is_B_mxfp8=False: ptr_D,
     )
     _register_if_available(
         K=k,
         num_experts=num_experts,
         is_B_int4=False,
+        is_B_mxfp4=False,
+        is_B_mxfp8=False)
 def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
                              num_rows_per_expert, n, k, num_experts, is_B_int4,
+                             is_B_mxfp4, is_B_mxfp8=False):
     expert_first_token_offset = torch.cat([
         torch.tensor([0],
                      dtype=num_rows_per_expert.dtype,
         K=k,
         num_experts=num_experts,
         is_B_int4=is_B_int4,
+        is_B_mxfp4=is_B_mxfp4,
+        is_B_mxfp8=is_B_mxfp8)
 def ceilDiv(a, b):
                   ep_size=1,
                   is_fp8=False,
                   is_int4=False,
+                  is_mxfp4=False,
+                  is_mxfp8=False):
     '''
     hidden_states: [num_rows, hidden_size]
     w13: [num_experts, 2*inter_size, hidden_size]
     num_experts: int
     is_int4: bool
     is_mxfp4: bool
+    is_mxfp8: bool
     '''
     # Resolve DTensors to local tensors before passing to custom ops
     # 4bits support [E, N, K]
     # other types [E, K, N]
+    if not is_int4 and not is_mxfp4 and not is_mxfp8:
         if not hasattr(w13, 'xpu_fused_moe'):
             if needs_transpose:
                 w13.data = w13.transpose(-1, -2).contiguous()
     ########### gemm1 ##################
     input_B = w13
+    if not is_fp8 and not is_int4 and not is_mxfp4 and not is_mxfp8:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             ptr_B=input_B,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     # act
     act_output = torch.empty((num_moe_inputs, inter_size),
     gemm2_output = torch.empty((num_moe_inputs, hidden_size),
                                dtype=hidden_states.dtype,
                                device=hidden_states.device)
+    if not is_fp8 and not is_int4 and not is_mxfp4 and not is_mxfp8:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             ptr_B=input_B,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     ops.moe_gather(output, gemm2_output, topk_weights,
                                 permuted_row_to_unpermuted_row,
         is_fp8 = getattr(self.experts, "is_fp8", False)
         is_int4 = getattr(self.experts, "is_int4", False)
         is_mxfp4 = getattr(self.experts, "is_mxfp4", False)
+        is_mxfp8 = getattr(self.experts, "is_mxfp8", False)
         w13_scales = getattr(self.experts, "gate_up_proj_scales", None)
         w2_scales = getattr(self.experts, "down_proj_scales", None)
             is_fp8=is_fp8,
             is_int4=is_int4,
             is_mxfp4=is_mxfp4,
+            is_mxfp8=is_mxfp8,
         )
         # All-reduce across EP group to combine partial expert outputs

build/torch213-cxx11-cu130-aarch64-linux/{_megablocks_cuda_18e92fb.abi3.so → _megablocks_cuda_c4d0dc5.abi3.so} RENAMED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c5ef8fb1c74800a4709e0008c8e2c2f7644188f1297ec96ca29b68bbe7c6b0f8
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build/torch213-cxx11-cu130-aarch64-linux/_ops.py CHANGED Viewed

@@ -1,9 +1,9 @@
 import torch
-from . import _megablocks_cuda_18e92fb
-ops = torch.ops._megablocks_cuda_18e92fb
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_megablocks_cuda_18e92fb::{op_name}"

 import torch
+from . import _megablocks_cuda_c4d0dc5
+ops = torch.ops._megablocks_cuda_c4d0dc5
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
+    return f"_megablocks_cuda_c4d0dc5::{op_name}"

build/torch213-cxx11-cu130-aarch64-linux/metadata.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "name": "megablocks",
-  "id": "_megablocks_cuda_18e92fb",
   "version": 1,
   "license": "Apache-2.0",
   "python-depends": [],
@@ -48,8 +48,8 @@
       "_layers/mpu.py": "BX61/pmqRCVeEMnzpuK8x5NpITBJyZfOawGnDegW1O8=",
       "_layers/router.py": "GKC/5//Uf43HT6zXlBTStCMTml2YXn7Jbgq9EgTgDC0=",
       "_layers/sharedexpert_registry.py": "GfwHKjbC7lBXYq9kNifp3HudTdZKSqwsFxGxFLc2krw=",
-      "_megablocks_cuda_18e92fb.abi3.so": "xe+PscdIAKRwngAIyOLC92RBiPEpfslsoptou+fGsPg=",
-      "_ops.py": "baTEjeHRnpTSLod30qZST/UL5GNv7vZoGeNwQWakxbo=",
       "_version.py": "W3l9oUrnmvfBoIy06Lv0y2ezTQhNU04ug/txHcdWJD0=",
       "backend/__init__.py": "4bunAqqjwE93nym8CigHbqVAal7UaYGdONb8lGRfrek=",
       "backend/kernels.py": "2UFNTlmSDk3S5y6UW14MJMPVVsVhmozxQ5wFDxtZHhQ=",
@@ -100,7 +100,7 @@
       "stk/random/__init__.py": "Gr/+Gl20jJ6fJVRfoAUxEcCPeChILQzrzhKVVAWQsRI=",
       "stk/random/random_ops.py": "AluIDj3Vo2ReKAsZz07yydbtndDbJoglekRGIaCoRZE=",
       "stk/random/random_ops_test.py": "B+a4Zb+OPhBtpVA48oc09sBL/AAQO9j/YUFI0nnXxzk=",
-      "xpu_fused_moe.py": "Dp6S3fK/Fc4Sr+Ac94vw2zz0v/QTGp6rYeMIUqE3Y/I="
     }
   },
   "provenance": {
@@ -110,7 +110,7 @@
       "dirty": false
     },
     "kernel": {
-      "sha": "18e92fb6886201ffb91c173f02593777756c793f",
       "dirty": false
     }
   }

 {
   "name": "megablocks",
+  "id": "_megablocks_cuda_c4d0dc5",
   "version": 1,
   "license": "Apache-2.0",
   "python-depends": [],
       "_layers/mpu.py": "BX61/pmqRCVeEMnzpuK8x5NpITBJyZfOawGnDegW1O8=",
       "_layers/router.py": "GKC/5//Uf43HT6zXlBTStCMTml2YXn7Jbgq9EgTgDC0=",
       "_layers/sharedexpert_registry.py": "GfwHKjbC7lBXYq9kNifp3HudTdZKSqwsFxGxFLc2krw=",
+      "_megablocks_cuda_c4d0dc5.abi3.so": "P17iD29WVEp2sEMOi2gE19+HD5fgnlWAn5FSn7D5xpo=",
+      "_ops.py": "9NSxkX5lSS5VhF9+hDazP5NimwZK2V/+nIXR0AQxnHA=",
       "_version.py": "W3l9oUrnmvfBoIy06Lv0y2ezTQhNU04ug/txHcdWJD0=",
       "backend/__init__.py": "4bunAqqjwE93nym8CigHbqVAal7UaYGdONb8lGRfrek=",
       "backend/kernels.py": "2UFNTlmSDk3S5y6UW14MJMPVVsVhmozxQ5wFDxtZHhQ=",
       "stk/random/__init__.py": "Gr/+Gl20jJ6fJVRfoAUxEcCPeChILQzrzhKVVAWQsRI=",
       "stk/random/random_ops.py": "AluIDj3Vo2ReKAsZz07yydbtndDbJoglekRGIaCoRZE=",
       "stk/random/random_ops_test.py": "B+a4Zb+OPhBtpVA48oc09sBL/AAQO9j/YUFI0nnXxzk=",
+      "xpu_fused_moe.py": "sPmuRSpkH1Bmk4hdpz53us8Qpc6yinT9rkld3T8WreI="
     }
   },
   "provenance": {
       "dirty": false
     },
     "kernel": {
+      "sha": "c4d0dc56661badb9cc47bde647e08fe3b4bf7458",
       "dirty": false
     }
   }

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build/torch213-cxx11-cu130-aarch64-linux/xpu_fused_moe.py CHANGED Viewed

@@ -26,7 +26,7 @@ def _register_xpu_fake_kernels():
     _register_if_available(
         "cutlass_grouped_gemm_interface",
-        lambda ptr_A, ptr_B, ptr_scales, ptr_bias, ptr_D, expert_first_token_offset, N, K, num_experts, is_B_int4, is_B_mxfp4: ptr_D,
     )
     _register_if_available(
@@ -106,12 +106,13 @@ def cutlass_grouped_gemm(input_A, input_B, bias, output, expert_token_count, n,
         K=k,
         num_experts=num_experts,
         is_B_int4=False,
-        is_B_mxfp4=False)
 def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
                              num_rows_per_expert, n, k, num_experts, is_B_int4,
-                             is_B_mxfp4):
     expert_first_token_offset = torch.cat([
         torch.tensor([0],
                      dtype=num_rows_per_expert.dtype,
@@ -129,7 +130,8 @@ def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
         K=k,
         num_experts=num_experts,
         is_B_int4=is_B_int4,
-        is_B_mxfp4=is_B_mxfp4)
 def ceilDiv(a, b):
@@ -206,7 +208,8 @@ def xpu_fused_moe(hidden_states,
                   ep_size=1,
                   is_fp8=False,
                   is_int4=False,
-                  is_mxfp4=False):
     '''
     hidden_states: [num_rows, hidden_size]
     w13: [num_experts, 2*inter_size, hidden_size]
@@ -228,6 +231,7 @@ def xpu_fused_moe(hidden_states,
     num_experts: int
     is_int4: bool
     is_mxfp4: bool
     '''
     # Resolve DTensors to local tensors before passing to custom ops
@@ -266,7 +270,7 @@ def xpu_fused_moe(hidden_states,
     # 4bits support [E, N, K]
     # other types [E, K, N]
-    if not is_int4 and not is_mxfp4:
         if not hasattr(w13, 'xpu_fused_moe'):
             if needs_transpose:
                 w13.data = w13.transpose(-1, -2).contiguous()
@@ -389,7 +393,7 @@ def xpu_fused_moe(hidden_states,
     ########### gemm1 ##################
     input_B = w13
-    if not is_fp8 and not is_int4 and not is_mxfp4:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             ptr_B=input_B,
@@ -401,7 +405,8 @@ def xpu_fused_moe(hidden_states,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
@@ -414,7 +419,8 @@ def xpu_fused_moe(hidden_states,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     # act
     act_output = torch.empty((num_moe_inputs, inter_size),
@@ -435,7 +441,7 @@ def xpu_fused_moe(hidden_states,
     gemm2_output = torch.empty((num_moe_inputs, hidden_size),
                                dtype=hidden_states.dtype,
                                device=hidden_states.device)
-    if not is_fp8 and not is_int4 and not is_mxfp4:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             ptr_B=input_B,
@@ -447,7 +453,8 @@ def xpu_fused_moe(hidden_states,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
@@ -460,7 +467,8 @@ def xpu_fused_moe(hidden_states,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     ops.moe_gather(output, gemm2_output, topk_weights,
                                 permuted_row_to_unpermuted_row,
@@ -633,6 +641,7 @@ class MegaBlocksMoeMLP(torch.nn.Module):
         is_fp8 = getattr(self.experts, "is_fp8", False)
         is_int4 = getattr(self.experts, "is_int4", False)
         is_mxfp4 = getattr(self.experts, "is_mxfp4", False)
         w13_scales = getattr(self.experts, "gate_up_proj_scales", None)
         w2_scales = getattr(self.experts, "down_proj_scales", None)
@@ -685,6 +694,7 @@ class MegaBlocksMoeMLP(torch.nn.Module):
             is_fp8=is_fp8,
             is_int4=is_int4,
             is_mxfp4=is_mxfp4,
         )
         # All-reduce across EP group to combine partial expert outputs

     _register_if_available(
         "cutlass_grouped_gemm_interface",
+        lambda ptr_A, ptr_B, ptr_scales, ptr_bias, ptr_D, expert_first_token_offset, N, K, num_experts, is_B_int4, is_B_mxfp4, is_B_mxfp8=False: ptr_D,
     )
     _register_if_available(
         K=k,
         num_experts=num_experts,
         is_B_int4=False,
+        is_B_mxfp4=False,
+        is_B_mxfp8=False)
 def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
                              num_rows_per_expert, n, k, num_experts, is_B_int4,
+                             is_B_mxfp4, is_B_mxfp8=False):
     expert_first_token_offset = torch.cat([
         torch.tensor([0],
                      dtype=num_rows_per_expert.dtype,
         K=k,
         num_experts=num_experts,
         is_B_int4=is_B_int4,
+        is_B_mxfp4=is_B_mxfp4,
+        is_B_mxfp8=is_B_mxfp8)
 def ceilDiv(a, b):
                   ep_size=1,
                   is_fp8=False,
                   is_int4=False,
+                  is_mxfp4=False,
+                  is_mxfp8=False):
     '''
     hidden_states: [num_rows, hidden_size]
     w13: [num_experts, 2*inter_size, hidden_size]
     num_experts: int
     is_int4: bool
     is_mxfp4: bool
+    is_mxfp8: bool
     '''
     # Resolve DTensors to local tensors before passing to custom ops
     # 4bits support [E, N, K]
     # other types [E, K, N]
+    if not is_int4 and not is_mxfp4 and not is_mxfp8:
         if not hasattr(w13, 'xpu_fused_moe'):
             if needs_transpose:
                 w13.data = w13.transpose(-1, -2).contiguous()
     ########### gemm1 ##################
     input_B = w13
+    if not is_fp8 and not is_int4 and not is_mxfp4 and not is_mxfp8:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             ptr_B=input_B,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     # act
     act_output = torch.empty((num_moe_inputs, inter_size),
     gemm2_output = torch.empty((num_moe_inputs, hidden_size),
                                dtype=hidden_states.dtype,
                                device=hidden_states.device)
+    if not is_fp8 and not is_int4 and not is_mxfp4 and not is_mxfp8:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             ptr_B=input_B,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     ops.moe_gather(output, gemm2_output, topk_weights,
                                 permuted_row_to_unpermuted_row,
         is_fp8 = getattr(self.experts, "is_fp8", False)
         is_int4 = getattr(self.experts, "is_int4", False)
         is_mxfp4 = getattr(self.experts, "is_mxfp4", False)
+        is_mxfp8 = getattr(self.experts, "is_mxfp8", False)
         w13_scales = getattr(self.experts, "gate_up_proj_scales", None)
         w2_scales = getattr(self.experts, "down_proj_scales", None)
             is_fp8=is_fp8,
             is_int4=is_int4,
             is_mxfp4=is_mxfp4,
+            is_mxfp8=is_mxfp8,
         )
         # All-reduce across EP group to combine partial expert outputs

build/torch213-cxx11-cu132-aarch64-linux/{_megablocks_cuda_18e92fb.abi3.so → _megablocks_cuda_c4d0dc5.abi3.so} RENAMED Viewed

@@ -1,3 +1,3 @@
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-oid sha256:da407707aebe565a1d9ba6d57d9b7e7437ca37e02a915b4e727b72fd38417b54
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+oid sha256:b771635991a5759a16294af9a8e125105fee84ff1f77cd412051adffbb24b343
 size 10393200

build/torch213-cxx11-cu132-aarch64-linux/_ops.py CHANGED Viewed

@@ -1,9 +1,9 @@
 import torch
-from . import _megablocks_cuda_18e92fb
-ops = torch.ops._megablocks_cuda_18e92fb
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_megablocks_cuda_18e92fb::{op_name}"

 import torch
+from . import _megablocks_cuda_c4d0dc5
+ops = torch.ops._megablocks_cuda_c4d0dc5
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
+    return f"_megablocks_cuda_c4d0dc5::{op_name}"

build/torch213-cxx11-cu132-aarch64-linux/metadata.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "name": "megablocks",
-  "id": "_megablocks_cuda_18e92fb",
   "version": 1,
   "license": "Apache-2.0",
   "python-depends": [],
@@ -48,8 +48,8 @@
       "_layers/mpu.py": "BX61/pmqRCVeEMnzpuK8x5NpITBJyZfOawGnDegW1O8=",
       "_layers/router.py": "GKC/5//Uf43HT6zXlBTStCMTml2YXn7Jbgq9EgTgDC0=",
       "_layers/sharedexpert_registry.py": "GfwHKjbC7lBXYq9kNifp3HudTdZKSqwsFxGxFLc2krw=",
-      "_megablocks_cuda_18e92fb.abi3.so": "2kB3B66+Vlodm6bVfZt+dDfKN+AqkVtOcnty/ThBe1Q=",
-      "_ops.py": "baTEjeHRnpTSLod30qZST/UL5GNv7vZoGeNwQWakxbo=",
       "_version.py": "W3l9oUrnmvfBoIy06Lv0y2ezTQhNU04ug/txHcdWJD0=",
       "backend/__init__.py": "4bunAqqjwE93nym8CigHbqVAal7UaYGdONb8lGRfrek=",
       "backend/kernels.py": "2UFNTlmSDk3S5y6UW14MJMPVVsVhmozxQ5wFDxtZHhQ=",
@@ -100,7 +100,7 @@
       "stk/random/__init__.py": "Gr/+Gl20jJ6fJVRfoAUxEcCPeChILQzrzhKVVAWQsRI=",
       "stk/random/random_ops.py": "AluIDj3Vo2ReKAsZz07yydbtndDbJoglekRGIaCoRZE=",
       "stk/random/random_ops_test.py": "B+a4Zb+OPhBtpVA48oc09sBL/AAQO9j/YUFI0nnXxzk=",
-      "xpu_fused_moe.py": "Dp6S3fK/Fc4Sr+Ac94vw2zz0v/QTGp6rYeMIUqE3Y/I="
     }
   },
   "provenance": {
@@ -110,7 +110,7 @@
       "dirty": false
     },
     "kernel": {
-      "sha": "18e92fb6886201ffb91c173f02593777756c793f",
       "dirty": false
     }
   }

 {
   "name": "megablocks",
+  "id": "_megablocks_cuda_c4d0dc5",
   "version": 1,
   "license": "Apache-2.0",
   "python-depends": [],
       "_layers/mpu.py": "BX61/pmqRCVeEMnzpuK8x5NpITBJyZfOawGnDegW1O8=",
       "_layers/router.py": "GKC/5//Uf43HT6zXlBTStCMTml2YXn7Jbgq9EgTgDC0=",
       "_layers/sharedexpert_registry.py": "GfwHKjbC7lBXYq9kNifp3HudTdZKSqwsFxGxFLc2krw=",
+      "_megablocks_cuda_c4d0dc5.abi3.so": "t3FjWZGldZoWKUr5qOElEF/uhP8fd81BIFGt/7sks0M=",
+      "_ops.py": "9NSxkX5lSS5VhF9+hDazP5NimwZK2V/+nIXR0AQxnHA=",
       "_version.py": "W3l9oUrnmvfBoIy06Lv0y2ezTQhNU04ug/txHcdWJD0=",
       "backend/__init__.py": "4bunAqqjwE93nym8CigHbqVAal7UaYGdONb8lGRfrek=",
       "backend/kernels.py": "2UFNTlmSDk3S5y6UW14MJMPVVsVhmozxQ5wFDxtZHhQ=",
       "stk/random/__init__.py": "Gr/+Gl20jJ6fJVRfoAUxEcCPeChILQzrzhKVVAWQsRI=",
       "stk/random/random_ops.py": "AluIDj3Vo2ReKAsZz07yydbtndDbJoglekRGIaCoRZE=",
       "stk/random/random_ops_test.py": "B+a4Zb+OPhBtpVA48oc09sBL/AAQO9j/YUFI0nnXxzk=",
+      "xpu_fused_moe.py": "sPmuRSpkH1Bmk4hdpz53us8Qpc6yinT9rkld3T8WreI="
     }
   },
   "provenance": {
       "dirty": false
     },
     "kernel": {
+      "sha": "c4d0dc56661badb9cc47bde647e08fe3b4bf7458",
       "dirty": false
     }
   }

build/torch213-cxx11-cu132-aarch64-linux/metadata.json.sigstore CHANGED Viewed

@@ -1 +1 @@

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build/torch213-cxx11-cu132-aarch64-linux/xpu_fused_moe.py CHANGED Viewed

@@ -26,7 +26,7 @@ def _register_xpu_fake_kernels():
     _register_if_available(
         "cutlass_grouped_gemm_interface",
-        lambda ptr_A, ptr_B, ptr_scales, ptr_bias, ptr_D, expert_first_token_offset, N, K, num_experts, is_B_int4, is_B_mxfp4: ptr_D,
     )
     _register_if_available(
@@ -106,12 +106,13 @@ def cutlass_grouped_gemm(input_A, input_B, bias, output, expert_token_count, n,
         K=k,
         num_experts=num_experts,
         is_B_int4=False,
-        is_B_mxfp4=False)
 def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
                              num_rows_per_expert, n, k, num_experts, is_B_int4,
-                             is_B_mxfp4):
     expert_first_token_offset = torch.cat([
         torch.tensor([0],
                      dtype=num_rows_per_expert.dtype,
@@ -129,7 +130,8 @@ def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
         K=k,
         num_experts=num_experts,
         is_B_int4=is_B_int4,
-        is_B_mxfp4=is_B_mxfp4)
 def ceilDiv(a, b):
@@ -206,7 +208,8 @@ def xpu_fused_moe(hidden_states,
                   ep_size=1,
                   is_fp8=False,
                   is_int4=False,
-                  is_mxfp4=False):
     '''
     hidden_states: [num_rows, hidden_size]
     w13: [num_experts, 2*inter_size, hidden_size]
@@ -228,6 +231,7 @@ def xpu_fused_moe(hidden_states,
     num_experts: int
     is_int4: bool
     is_mxfp4: bool
     '''
     # Resolve DTensors to local tensors before passing to custom ops
@@ -266,7 +270,7 @@ def xpu_fused_moe(hidden_states,
     # 4bits support [E, N, K]
     # other types [E, K, N]
-    if not is_int4 and not is_mxfp4:
         if not hasattr(w13, 'xpu_fused_moe'):
             if needs_transpose:
                 w13.data = w13.transpose(-1, -2).contiguous()
@@ -389,7 +393,7 @@ def xpu_fused_moe(hidden_states,
     ########### gemm1 ##################
     input_B = w13
-    if not is_fp8 and not is_int4 and not is_mxfp4:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             ptr_B=input_B,
@@ -401,7 +405,8 @@ def xpu_fused_moe(hidden_states,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
@@ -414,7 +419,8 @@ def xpu_fused_moe(hidden_states,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     # act
     act_output = torch.empty((num_moe_inputs, inter_size),
@@ -435,7 +441,7 @@ def xpu_fused_moe(hidden_states,
     gemm2_output = torch.empty((num_moe_inputs, hidden_size),
                                dtype=hidden_states.dtype,
                                device=hidden_states.device)
-    if not is_fp8 and not is_int4 and not is_mxfp4:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             ptr_B=input_B,
@@ -447,7 +453,8 @@ def xpu_fused_moe(hidden_states,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
@@ -460,7 +467,8 @@ def xpu_fused_moe(hidden_states,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
-            is_B_mxfp4=is_mxfp4)
     ops.moe_gather(output, gemm2_output, topk_weights,
                                 permuted_row_to_unpermuted_row,
@@ -633,6 +641,7 @@ class MegaBlocksMoeMLP(torch.nn.Module):
         is_fp8 = getattr(self.experts, "is_fp8", False)
         is_int4 = getattr(self.experts, "is_int4", False)
         is_mxfp4 = getattr(self.experts, "is_mxfp4", False)
         w13_scales = getattr(self.experts, "gate_up_proj_scales", None)
         w2_scales = getattr(self.experts, "down_proj_scales", None)
@@ -685,6 +694,7 @@ class MegaBlocksMoeMLP(torch.nn.Module):
             is_fp8=is_fp8,
             is_int4=is_int4,
             is_mxfp4=is_mxfp4,
         )
         # All-reduce across EP group to combine partial expert outputs

     _register_if_available(
         "cutlass_grouped_gemm_interface",
+        lambda ptr_A, ptr_B, ptr_scales, ptr_bias, ptr_D, expert_first_token_offset, N, K, num_experts, is_B_int4, is_B_mxfp4, is_B_mxfp8=False: ptr_D,
     )
     _register_if_available(
         K=k,
         num_experts=num_experts,
         is_B_int4=False,
+        is_B_mxfp4=False,
+        is_B_mxfp8=False)
 def cutlass_grouped_gemm_xe2(input_A, input_B, scales, bias, output,
                              num_rows_per_expert, n, k, num_experts, is_B_int4,
+                             is_B_mxfp4, is_B_mxfp8=False):
     expert_first_token_offset = torch.cat([
         torch.tensor([0],
                      dtype=num_rows_per_expert.dtype,
         K=k,
         num_experts=num_experts,
         is_B_int4=is_B_int4,
+        is_B_mxfp4=is_B_mxfp4,
+        is_B_mxfp8=is_B_mxfp8)
 def ceilDiv(a, b):
                   ep_size=1,
                   is_fp8=False,
                   is_int4=False,
+                  is_mxfp4=False,
+                  is_mxfp8=False):
     '''
     hidden_states: [num_rows, hidden_size]
     w13: [num_experts, 2*inter_size, hidden_size]
     num_experts: int
     is_int4: bool
     is_mxfp4: bool
+    is_mxfp8: bool
     '''
     # Resolve DTensors to local tensors before passing to custom ops
     # 4bits support [E, N, K]
     # other types [E, K, N]
+    if not is_int4 and not is_mxfp4 and not is_mxfp8:
         if not hasattr(w13, 'xpu_fused_moe'):
             if needs_transpose:
                 w13.data = w13.transpose(-1, -2).contiguous()
     ########### gemm1 ##################
     input_B = w13
+    if not is_fp8 and not is_int4 and not is_mxfp4 and not is_mxfp8:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             ptr_B=input_B,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=gemm1_input,
             K=hidden_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     # act
     act_output = torch.empty((num_moe_inputs, inter_size),
     gemm2_output = torch.empty((num_moe_inputs, hidden_size),
                                dtype=hidden_states.dtype,
                                device=hidden_states.device)
+    if not is_fp8 and not is_int4 and not is_mxfp4 and not is_mxfp8:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             ptr_B=input_B,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     else:
         ops.cutlass_grouped_gemm_interface(
             ptr_A=input_A,
             K=inter_size,
             num_experts=num_experts_per_node,
             is_B_int4=is_int4,
+            is_B_mxfp4=is_mxfp4,
+            is_B_mxfp8=is_mxfp8)
     ops.moe_gather(output, gemm2_output, topk_weights,
                                 permuted_row_to_unpermuted_row,
         is_fp8 = getattr(self.experts, "is_fp8", False)
         is_int4 = getattr(self.experts, "is_int4", False)
         is_mxfp4 = getattr(self.experts, "is_mxfp4", False)
+        is_mxfp8 = getattr(self.experts, "is_mxfp8", False)
         w13_scales = getattr(self.experts, "gate_up_proj_scales", None)
         w2_scales = getattr(self.experts, "down_proj_scales", None)
             is_fp8=is_fp8,
             is_int4=is_int4,
             is_mxfp4=is_mxfp4,
+            is_mxfp8=is_mxfp8,
         )
         # All-reduce across EP group to combine partial expert outputs