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unsloth/kernels/moe/grouped_gemm/reference/layers/qwen3_moe.py

@@ -37,6 +37,19 @@ NOTE: This is NOT to be used for production as it contains many extra checks and
 
 @dataclass
 class GroupedGEMMResult:
+    """Container for storing intermediate and final results from grouped GEMM operations.
+
+    Attributes:
+        token_counts_by_expert: Number of tokens assigned to each expert
+        gather_indices: Indices used for token permutation and unpermutation
+        topk_weights: Routing weights for the top-k selected experts
+        first_gemm: Output from the first grouped matrix multiplication
+        intermediate: Result after applying activation function and element-wise multiplication
+        second_gemm: Output from the second grouped matrix multiplication
+        hidden_states_unpermute: Hidden states after unpermutation from expert order to token order
+        hidden_states: Final output hidden states
+    """
+
     token_counts_by_expert: torch.Tensor
     gather_indices: torch.Tensor
     topk_weights: torch.Tensor
@@ -48,6 +61,12 @@ class GroupedGEMMResult:
 
 
 class Qwen3MoeGroupedGEMMBlock(torch.nn.Module):
+    """Reference implementation of Qwen3 Mixture of Experts block using grouped GEMM operations.
+
+    This implementation uses torch-native operations and stores intermediate results for debugging.
+    It implements the MoE routing mechanism with top-k expert selection and grouped matrix multiplications.
+    """
+
     def __init__(
         self,
         config,
@@ -55,6 +74,14 @@ class Qwen3MoeGroupedGEMMBlock(torch.nn.Module):
         gate_up_proj: torch.Tensor,
         down_proj: torch.Tensor,
     ):
+        """Initialize the Qwen3 MoE block with expert weights.
+
+        Args:
+            config: Qwen3MoeConfig containing model configuration parameters
+            gate: Router gate weights for expert selection [num_experts, hidden_size]
+            gate_up_proj: Combined gate and up projection weights [num_experts, 2*moe_intermediate_size, hidden_size]
+            down_proj: Down projection weights [num_experts, hidden_size, moe_intermediate_size]
+        """
         super().__init__()
         self.num_experts = config.num_experts
         self.top_k = config.num_experts_per_tok
@@ -84,6 +111,17 @@ class Qwen3MoeGroupedGEMMBlock(torch.nn.Module):
 
     @staticmethod
     def extract_hf_weights(moe_block: Qwen3MoeSparseMoeBlock):
+        """Extract and reorganize weights from a HuggingFace Qwen3MoeSparseMoeBlock.
+
+        Args:
+            moe_block: HuggingFace Qwen3MoeSparseMoeBlock instance
+
+        Returns:
+            Tuple containing:
+                - gate: Router gate weights
+                - gate_up_proj: Combined gate and up projection weights
+                - down_proj: Down projection weights
+        """
         config: Qwen3MoeConfig = moe_block.experts[0].config
         num_experts = config.num_experts
 
@@ -105,11 +143,24 @@ class Qwen3MoeGroupedGEMMBlock(torch.nn.Module):
 
     @classmethod
     def from_hf(cls, moe_block: Qwen3MoeSparseMoeBlock):
+        """Create a Qwen3MoeGroupedGEMMBlock from a HuggingFace MoE block.
+
+        Args:
+            moe_block: HuggingFace Qwen3MoeSparseMoeBlock instance
+
+        Returns:
+            Qwen3MoeGroupedGEMMBlock instance with extracted weights
+        """
         config: Qwen3MoeConfig = moe_block.experts[0].config
         gate, gate_up_proj, down_proj = cls.extract_hf_weights(moe_block)
         return cls(config, gate, gate_up_proj, down_proj)
 
     def check_weights(self, moe_block: Qwen3MoeSparseMoeBlock):
+        """Verify that the weights match those in the original HuggingFace MoE block.
+
+        Args:
+            moe_block: HuggingFace Qwen3MoeSparseMoeBlock to compare against
+        """
         for i in range(self.num_experts):
             assert self.gate_up_proj[i].equal(
                 torch.cat(
@@ -123,12 +174,31 @@ class Qwen3MoeGroupedGEMMBlock(torch.nn.Module):
             assert self.down_proj[i].equal(moe_block.experts[i].down_proj.weight.data)
 
     def act_and_mul(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply activation function to gate projection and multiply with up projection.
+
+        Args:
+            x: Input tensor with shape [..., 2 * moe_intermediate_size]
+
+        Returns:
+            Result of activation(gate_proj) * up_proj with shape [..., moe_intermediate_size]
+        """
         assert x.shape[-1] == 2 * self.moe_intermediate_size
         gate_proj = x[..., : self.moe_intermediate_size]
         up_proj = x[..., self.moe_intermediate_size :]
         return self.act_fn(gate_proj) * up_proj
 
     def run_router(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        """Run the routing mechanism to select top-k experts for each token.
+
+        Args:
+            hidden_states: Input hidden states [batch_size * seq_len, hidden_size]
+
+        Returns:
+            Tuple containing:
+                - router_logits: Raw logits from the router
+                - routing_weights: Normalized weights for selected experts
+                - selected_experts: Indices of selected experts for each token
+        """
         # router_logits: (batch * sequence_length, n_experts)
         router_logits = torch.nn.functional.linear(hidden_states, self.gate)
 
@@ -146,6 +216,16 @@ class Qwen3MoeGroupedGEMMBlock(torch.nn.Module):
     def get_token_counts_and_gather_indices(
         self, selected_experts: torch.Tensor
     ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Compute token counts per expert and gather indices for permutation.
+
+        Args:
+            selected_experts: Indices of selected experts for each token
+
+        Returns:
+            Tuple containing:
+                - token_counts_by_expert: Number of tokens assigned to each expert
+                - gather_indices: Indices for permuting tokens from token order to expert order
+        """
         token_counts_by_expert, gather_indices = get_routing_indices(
             selected_experts, self.num_experts
         )
@@ -154,7 +234,16 @@ class Qwen3MoeGroupedGEMMBlock(torch.nn.Module):
         return token_counts_by_expert, gather_indices
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        """ """
+        """Forward pass through the MoE block.
+
+        Args:
+            hidden_states: Input tensor [batch_size, seq_len, hidden_size]
+
+        Returns:
+            Tuple containing:
+                - GroupedGEMMResult: Container with all intermediate results
+                - router_logits: Raw routing logits for auxiliary loss computation
+        """
         batch_size, sequence_length, hidden_dim = hidden_states.shape
         num_tokens = batch_size * sequence_length
         total_tokens = num_tokens * self.top_k
@@ -214,6 +303,12 @@ class Qwen3MoeGroupedGEMMBlock(torch.nn.Module):
 
 
 class Qwen3MoeFusedGroupedGEMMBlock(Qwen3MoeGroupedGEMMBlock):
+    """Optimized Qwen3 MoE block using fused grouped GEMM kernels.
+
+    This implementation uses Triton-based grouped GEMM kernels for improved performance
+    and supports various optimization options like permutation fusion and kernel tuning.
+    """
+
     def __init__(
         self,
         config: Qwen3MoeConfig,
@@ -229,6 +324,22 @@ class Qwen3MoeFusedGroupedGEMMBlock(Qwen3MoeGroupedGEMMBlock):
         dW_only: bool = False,
         dX_only: bool = False,
     ):
+        """Initialize the fused grouped GEMM MoE block.
+
+        Args:
+            config: Qwen3MoeConfig containing model configuration
+            gate: Router gate weights
+            gate_up_proj: Combined gate and up projection weights
+            down_proj: Down projection weights
+            permute_x: Whether to fuse input permutation in the first GEMM
+            permute_y: Whether to fuse output unpermutation in the second GEMM
+            autotune: Whether to automatically tune kernel configurations
+            kernel_config_fwd: Manual kernel configuration for forward pass
+            kernel_config_bwd_dW: Manual kernel configuration for weight gradients
+            kernel_config_bwd_dX: Manual kernel configuration for input gradients
+            dW_only: Whether to compute only weight gradients
+            dX_only: Whether to compute only input gradients
+        """
         super().__init__(config, gate, gate_up_proj, down_proj)
         self.permute_x = permute_x
         self.permute_y = permute_y
@@ -258,6 +369,22 @@ class Qwen3MoeFusedGroupedGEMMBlock(Qwen3MoeGroupedGEMMBlock):
         dW_only: bool = False,
         dX_only: bool = False,
     ):
+        """Create a fused grouped GEMM MoE block from a HuggingFace MoE block.
+
+        Args:
+            moe_block: HuggingFace Qwen3MoeSparseMoeBlock instance
+            permute_x: Whether to fuse input permutation in the first GEMM
+            permute_y: Whether to fuse output unpermutation in the second GEMM
+            autotune: Whether to automatically tune kernel configurations
+            kernel_config_fwd: Manual kernel configuration for forward pass
+            kernel_config_bwd_dW: Manual kernel configuration for weight gradients
+            kernel_config_bwd_dX: Manual kernel configuration for input gradients
+            dW_only: Whether to compute only weight gradients
+            dX_only: Whether to compute only input gradients
+
+        Returns:
+            Qwen3MoeFusedGroupedGEMMBlock instance with extracted weights and configurations
+        """
         config: Qwen3MoeConfig = moe_block.experts[0].config
         gate, gate_up_proj, down_proj = Qwen3MoeGroupedGEMMBlock.extract_hf_weights(
             moe_block
@@ -278,6 +405,16 @@ class Qwen3MoeFusedGroupedGEMMBlock(Qwen3MoeGroupedGEMMBlock):
         )
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        """Forward pass using fused grouped GEMM kernels.
+
+        Args:
+            hidden_states: Input tensor [batch_size, seq_len, hidden_size]
+
+        Returns:
+            Tuple containing:
+                - hidden_states: Output tensor [batch_size, seq_len, hidden_size]
+                - router_logits: Raw routing logits for auxiliary loss computation
+        """
         batch_size, sequence_length, hidden_dim = hidden_states.shape
         num_tokens = batch_size * sequence_length
         total_tokens = num_tokens * self.top_k