Update docs

deep_gemm/jit_kernels/gemm.py

@@ -157,11 +157,14 @@ def gemm_fp8_fp8_bf16_nt(lhs: Tuple[torch.Tensor, torch.Tensor],
                          rhs: Tuple[torch.Tensor, torch.Tensor],
                          out: torch.Tensor) -> None:
     """
-    Do a normal GEMM with FP8 inputs and BF16 output, with 1x128 LHS scaling and 128x128 RHS scaling.
-    LHS, RHS, RHS scaling factors, and output tensors must be in contiguous format.
-    RHS and RHS scaling factors are required to be transposed.
-    The LHS scaling tensor requires a TMA-aligned transposed format, if your input does not match the requirement,
-        this function will do a transposing with a set of slow PyTorch operations.
+    Perform a normal GEMM with FP8 inputs and BF16 output, with 1x128 LHS scaling and 128x128 RHS scaling.
+
+    Requirements:
+        LHS, RHS, and output tensors must be contiguous in dimension 1, i.e., stride(1) = 1.
+        The stride(0) of LHS and RHS must be a multiple of 16, and the stride(0) of output must be a multiple of 8.
+        RHS and RHS scaling factors are required to be transposed.
+        The LHS scaling tensor requires a TMA-aligned transposed format, if your input does not match the requirement,
+            this function will do a transposing with a set of slow PyTorch operations.
 
     Arguments:
         lhs: the first element is an FP8 tensor (typed `torch.float8_e4m3fn`) of shape `[m, k]`,

deep_gemm/jit_kernels/m_grouped_gemm.py

@@ -14,13 +14,15 @@ def m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(lhs: Tuple[torch.Tensor, torch.Ten
                                               rhs: Tuple[torch.Tensor, torch.Tensor],
                                               out: torch.Tensor, m_indices: torch.Tensor) -> None:
     """
-    Do a grouped GEMM (contiguous format) with FP8 inputs and BF16 output, with 1x128 LHS scaling and 128x128 RHS scaling.
-    LHS, RHS, RHS scaling factors, and output tensors must be in contiguous format.
-    RHS and RHS scaling factors are required to be transposed.
-    The LHS scaling tensor requires a TMA-aligned transposed format, if your input does not match the requirement,
-        this function will do a transposing with a set of slow PyTorch operations.
-    On the M axis, inputs are grouped into several batches, of which batch sizes aligned to
-        `get_m_alignment_for_contiguous_layout()` (128).
+    Perform a grouped GEMM (contiguous format) with FP8 inputs and BF16 output, with 1x128 LHS scaling and 128x128 RHS scaling.
+
+    Requirements:
+        LHS, RHS, RHS scaling factors, and output tensors must be in contiguous format.
+        RHS and RHS scaling factors are required to be transposed.
+        The LHS scaling tensor requires a TMA-aligned transposed format, if your input does not match the requirement,
+            this function will do a transposing with a set of slow PyTorch operations.
+        On the M axis, inputs are grouped into several batches, of which batch sizes aligned to
+            `get_m_alignment_for_contiguous_layout()` (128).
 
     Arguments:
         lhs: the first element is an FP8 tensor (typed `torch.float8_e4m3fn`) of shape `[m_sum, k]`,
@@ -116,13 +118,15 @@ def m_grouped_gemm_fp8_fp8_bf16_nt_masked(lhs: Tuple[torch.Tensor, torch.Tensor]
                                           rhs: Tuple[torch.Tensor, torch.Tensor],
                                           out: torch.Tensor, masked_m: torch.Tensor, expected_m: int) -> None:
     """
-    Do a grouped GEMM (masked format) with FP8 inputs and BF16 output, with 1x128 LHS scaling and 128x128 RHS scaling.
-    LHS, RHS, RHS scaling factors, and output tensors must be in contiguous format.
-    RHS and RHS scaling factors are required to be transposed.
-    The LHS scaling tensor requires a TMA-aligned transposed format, if your input does not match the requirement,
-        this function will do a transposing with a set of slow PyTorch operations.
-    Moreover, this alignment requirement is different with the contiguous-format kernel, as we require that each batch
-        should be separately transposed.
+    Perform a grouped GEMM (masked format) with FP8 inputs and BF16 output, with 1x128 LHS scaling and 128x128 RHS scaling.
+
+    Requirements:
+        LHS, RHS, RHS scaling factors, and output tensors must be in contiguous format.
+        RHS and RHS scaling factors are required to be transposed.
+        The LHS scaling tensor requires a TMA-aligned transposed format, if your input does not match the requirement,
+            this function will do a transposing with a set of slow PyTorch operations.
+        Moreover, this alignment requirement is different with the contiguous-format kernel, as we require that each batch
+            should be separately transposed.
 
     Arguments:
         lhs: the first element is an FP8 tensor (typed `torch.float8_e4m3fn`) of shape `[num_groups, m_max, k]`,

deep_gemm/jit_kernels/wgrad_gemm.py

@@ -15,18 +15,22 @@ def wgrad_gemm_fp8_fp8_fp32_nt(lhs: Tuple[torch.Tensor, torch.Tensor],
                                rhs: Tuple[torch.Tensor, torch.Tensor],
                                out: Tuple[torch.Tensor, torch.Tensor]):
     """
-    Do a weight gradient GEMM with FP8 inputs and FP32 output, with 1x128 LHS scaling and 1x128 RHS scaling.
-    LHS, RHS, and output tensors must be contiguous in dimension 1, i.e., stride(1) = 1.
-    RHS and RHS scaling factors are required to be transposed.
-    The LHS scaling and RHS scaling tensor require TMA-aligned transposed format, if your input does not match the requirement,
-        this function will do a transposing with a set of slow PyTorch operations.
+    Perform a weight gradient GEMM with FP8 inputs and FP32 output, with 1x128 LHS scaling and 1x128 RHS scaling.
+        Results will be accumulated into the output tensor.
+
+    Requirements:
+        LHS, RHS, and output tensors must be contiguous in dimension 1, i.e., stride(1) = 1.
+        The stride(0) of LHS and RHS must be a multiple of 16, and the stride(0) of output must be a multiple of 4.
+        RHS and RHS scaling factors are required to be transposed.
+        The LHS scaling and RHS scaling tensor require TMA-aligned transposed format, if your input does not match the requirement,
+            this function will do a transposing with a set of slow PyTorch operations.
 
     Arguments:
         lhs: the first element is an FP8 tensor (typed `torch.float8_e4m3fn`) of shape `[m, k]`,
              the second element is an FP32 1x128 scaling tensor for LHS of shape `[m, ⌈k / 128⌉]`.
         rhs: the first element is an FP8 tensor (typed `torch.float8_e4m3fn`) of shape `[n, k]`,
              the second element is an FP32 1x128 scaling tensor for RHS of shape `[n, ⌈k / 128⌉]`.
-        out: the FP32 output tensor of shape `[m, n]`, representing the result.
+        out: the FP32 output tensor of shape `[m, n]`, which will be accumulated.
     """
     lhs, lhs_scales = lhs
     rhs, rhs_scales = rhs
@@ -131,10 +135,13 @@ def k_grouped_wgrad_gemm_fp8_fp8_fp32_nt(lhs: Tuple[torch.Tensor, torch.Tensor],
                                          batch_sizes: List[int]):
     """
     Perform a k-grouped weight gradient GEMM with FP8 inputs and FP32 output, with 1x128 LHS scaling and 1x128 RHS scaling.
-    This function handles multiple batches with varying k-dimensions, processing each batch sequentially.
-    Each batch's LHS, RHS, and output tensors must be contiguous.
-    The RHS and RHS scaling factors are required to be transposed.
-    The LHS scaling and RHS scaling tensors require TMA-aligned transposed format.
+        Results will be accumulated into the output tensor.
+
+    Requirements:
+        This function handles multiple batches with varying k-dimensions, processing each batch sequentially.
+        Each batch's LHS, RHS, and output tensors must be contiguous.
+        The RHS and RHS scaling factors are required to be transposed.
+        The LHS scaling and RHS scaling tensors require TMA-aligned transposed format.
 
     Arguments:
         lhs: the first element is a flattened FP8 tensor (typed `torch.float8_e4m3fn`) containing all batches of LHS data,
@@ -145,7 +152,7 @@ def k_grouped_wgrad_gemm_fp8_fp8_fp32_nt(lhs: Tuple[torch.Tensor, torch.Tensor],
                  and the flattened shape is `[sum(n * k for k in batch_sizes)]`, where n is the number of rows.
              the second element is an FP32 scaling tensor for RHS with shape `[⌈k / 128⌉ for k in batch_sizes), n]`,
                  representing the per-128-channel scaling factors.
-        out: The FP32 output tensor of shape [num_batches, m, n], representing the result.
+        out: The FP32 output tensor of shape [num_batches, m, n], which will be accumulated.
         batch_sizes: A list of integers specifying the k-dimension for each batch.
     """
     lhs, lhs_scales = lhs[0].view(-1), lhs[1]