[Kernel] Support W8A8 channel-wise weights and per-token activations in triton fused_moe_kernel (#16366)

Signed-off-by: mgoin <mgoin64@gmail.com>

tests/kernels/utils_block.py

@@ -0,0 +1,63 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import torch
+
+
+def native_w8a8_block_matmul(A: torch.Tensor, B: torch.Tensor,
+                             As: torch.Tensor, Bs: torch.Tensor, block_size,
+                             output_dtype):
+    """This function performs matrix multiplication with block-wise
+    quantization using native torch.
+    It is agnostic to the input data type and can be used for both int8 and
+    fp8 data types.
+
+    It takes two input tensors `A` and `B` (int8) with scales `As` and 
+    `Bs` (float32).
+    The output is returned in the specified `output_dtype`.
+    """
+    A = A.to(torch.float32)
+    B = B.to(torch.float32)
+    assert A.shape[-1] == B.shape[-1]
+    assert B.ndim == 2 and B.is_contiguous() and Bs.ndim == 2
+    assert len(block_size) == 2
+    block_n, block_k = block_size[0], block_size[1]
+    assert (A.shape[-1] + block_k - 1) // block_k == As.shape[-1]
+    assert A.shape[:-1] == As.shape[:-1]
+
+    M = A.numel() // A.shape[-1]
+    N, K = B.shape
+    origin_C_shape = A.shape[:-1] + (N, )
+    A = A.reshape(M, A.shape[-1])
+    As = As.reshape(M, As.shape[-1])
+    n_tiles = (N + block_n - 1) // block_n
+    k_tiles = (K + block_k - 1) // block_k
+    assert n_tiles == Bs.shape[0]
+    assert k_tiles == Bs.shape[1]
+
+    C_shape = (M, N)
+    C = torch.zeros(C_shape, dtype=torch.float32, device=A.device)
+
+    A_tiles = [
+        A[:, i * block_k:min((i + 1) * block_k, K)] for i in range(k_tiles)
+    ]
+    B_tiles = [[
+        B[
+            j * block_n:min((j + 1) * block_n, N),
+            i * block_k:min((i + 1) * block_k, K),
+        ] for i in range(k_tiles)
+    ] for j in range(n_tiles)]
+    C_tiles = [
+        C[:, j * block_n:min((j + 1) * block_n, N)] for j in range(n_tiles)
+    ]
+    As_tiles = [As[:, i:i + 1] for i in range(k_tiles)]
+
+    for i in range(k_tiles):
+        for j in range(n_tiles):
+            a = A_tiles[i]
+            b = B_tiles[j][i]
+            c = C_tiles[j]
+            s = As_tiles[i] * Bs[j][i]
+            c[:, :] += torch.matmul(a, b.t()) * s
+
+    C = C.reshape(origin_C_shape).to(output_dtype)
+    return C