diff --git a/pyproject.toml b/pyproject.toml
index 7aeba41..c359f69 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -33,6 +33,7 @@ dependencies = [
 packages = [
   "world_engine",
   "world_engine.model",
+  "world_engine.kernels",
 ]
 
 [tool.setuptools.package-dir]
diff --git a/src/kernels/__init__.py b/src/kernels/__init__.py
new file mode 100644
index 0000000..e4465f3
--- /dev/null
+++ b/src/kernels/__init__.py
@@ -0,0 +1,9 @@
+from .grouped_gemm import grouped_gemm
+from .moe import index_shuffling, scatter_add_dense_tokens
+
+
+__all__ = [
+    "grouped_gemm",
+    "index_shuffling",
+    "scatter_add_dense_tokens",
+]
diff --git a/src/kernels/grouped_gemm.py b/src/kernels/grouped_gemm.py
new file mode 100644
index 0000000..f7f5f11
--- /dev/null
+++ b/src/kernels/grouped_gemm.py
@@ -0,0 +1,382 @@
+"""
+yoinked from https://github.com/pytorch/FBGEMM/blob/main/fbgemm_gpu/experimental/gemm/triton_gemm/grouped_gemm.py
+"""
+
+
+from __future__ import annotations
+
+import inspect
+import warnings
+from typing import Optional
+
+import torch
+import triton
+import triton.language as tl
+from triton.runtime import driver
+
+
+_NV_CONFIGS = [
+    triton.Config(
+        {
+            "BLOCK_SIZE_M": block_size_m,
+            "BLOCK_SIZE_N": block_size_n,
+            "BLOCK_SIZE_K": block_size_k,
+            "NUM_CONSUMER_GROUPS": 1,
+        },
+        num_stages=num_stages,
+        num_warps=num_warps,
+        num_ctas=1,
+    )
+    for block_size_m in [64, 128]
+    for block_size_n in [64, 128, 256]
+    for block_size_k in [64, 128, 256]
+    for num_stages in [3, 4]
+    for num_warps in [4, 8]
+]
+
+_AMD_CONFIGS = [
+    triton.Config(
+        {
+            "BLOCK_SIZE_M": block_size_m,
+            "BLOCK_SIZE_N": block_size_n,
+            "BLOCK_SIZE_K": block_size_k,
+            "waves_per_eu": waves_per_cu,
+            "matrix_instr_nonkdim": matrix_instr_nonkdim,
+            "NUM_CONSUMER_GROUPS": 1,
+        },
+        num_stages=num_stages,
+        num_warps=num_warps,
+    )
+    for block_size_m in [32, 64, 128]
+    for block_size_n in [32, 64, 128, 256]
+    for block_size_k in [128, 256]
+    for num_stages in [1, 2]
+    for num_warps, waves_per_cu in [(4, 1), (8, 2), (16, 4)]
+    for matrix_instr_nonkdim in [16]
+]
+
+HAS_TMA_DESC = "nv_tma_desc_type" in dir(tl)
+_HAS_WS_SUPPORT = None
+
+
+def _check_ws_support() -> bool:
+    if not hasattr(tl, "async_task"):
+        return False
+    config_signature = inspect.signature(triton.Config).parameters
+    if (
+        "num_consumer_groups" not in config_signature
+        or "num_buffers_warp_spec" not in config_signature
+    ):
+        return False
+    return HAS_TMA_DESC
+
+
+def _set_ws_support() -> None:
+    global _HAS_WS_SUPPORT
+    if _HAS_WS_SUPPORT is None:
+        _HAS_WS_SUPPORT = _check_ws_support()
+
+
+_set_ws_support()
+
+
+class TmaAutoTuneHelper:
+    class KernelParamWrapper:
+        def __init__(self, desc: torch.Tensor):
+            self.desc = desc
+
+        def tma_desc_cpu_ptr(self) -> int:
+            return self.desc.data_ptr()
+
+    TMA_SIZE = 128
+
+    def __init__(self):
+        self.fill_1d_tma_descriptor_inner = driver.active.utils.fill_1d_tma_descriptor
+        self.fill_2d_tma_descriptor_inner = driver.active.utils.fill_2d_tma_descriptor
+        if HAS_TMA_DESC:
+            self.descriptors: dict[str, torch.Tensor] = {}
+        else:
+            self.cuda_descriptors: dict[str, torch.Tensor] = {}
+
+    def init_tma_descriptor(self, name: str) -> None:
+        if HAS_TMA_DESC:
+            self.descriptors[name] = torch.empty(self.TMA_SIZE, device="cpu", dtype=torch.int8)
+        else:
+            self.cuda_descriptors[name] = torch.empty(self.TMA_SIZE, device="cuda", dtype=torch.int8)
+
+    def fill_1d_tma_descriptor(self, name: str, ptr: int, dim: int, block_dim: int, element_size: int) -> None:
+        if HAS_TMA_DESC:
+            desc = self.descriptors[name]
+            assert desc.data_ptr() % 64 == 0
+            self.fill_1d_tma_descriptor_inner(ptr, dim, block_dim, element_size, desc.data_ptr())
+        else:
+            desc = self.cuda_descriptors[name]
+            buf = torch.empty_like(desc, device="cpu", pin_memory=True)
+            self.fill_1d_tma_descriptor_inner(ptr, dim, block_dim, element_size, buf.data_ptr())
+            desc.copy_(buf, non_blocking=True)
+
+    def fill_2d_tma_descriptor(
+        self,
+        name: str,
+        ptr: int,
+        dim1: int,
+        dim0: int,
+        block_dim1: int,
+        block_dim0: int,
+        element_size: int,
+    ) -> None:
+        if HAS_TMA_DESC:
+            desc = self.descriptors[name]
+            assert desc.data_ptr() % 64 == 0
+            self.fill_2d_tma_descriptor_inner(
+                ptr, dim1, dim0, block_dim1, block_dim0, element_size, desc.data_ptr()
+            )
+        else:
+            desc = self.cuda_descriptors[name]
+            buf = torch.empty_like(desc, device="cpu", pin_memory=True)
+            self.fill_2d_tma_descriptor_inner(
+                ptr, dim1, dim0, block_dim1, block_dim0, element_size, buf.data_ptr()
+            )
+            desc.copy_(buf, non_blocking=True)
+
+    def get_tma_descriptor_kernel_param(self, name: str):
+        if HAS_TMA_DESC:
+            return self.KernelParamWrapper(self.descriptors[name])
+        return self.cuda_descriptors[name]
+
+
+@triton.autotune(
+    configs=_AMD_CONFIGS if torch.version.hip else _NV_CONFIGS,
+    key=["G", "M_BUCKET", "N", "K"],
+    restore_value=["c_ptr"],
+)
+@triton.jit
+def _fbgemm_grouped_gemm(
+    a_desc_ptr,
+    b_desc_ptr,
+    c_ptr,
+    scatter_add_indices,
+    m_sizes,
+    G: tl.constexpr,
+    M_BUCKET,
+    N: tl.constexpr,
+    K: tl.constexpr,
+    NUM_SMS: tl.constexpr,
+    FUSE_SCATTER_ADD: tl.constexpr,
+    USE_TMA_LOAD: tl.constexpr,
+    USE_TMA_STORE: tl.constexpr,
+    USE_FAST_ACCUM: tl.constexpr,
+    BLOCK_SIZE_M: tl.constexpr,
+    BLOCK_SIZE_N: tl.constexpr,
+    BLOCK_SIZE_K: tl.constexpr,
+    NUM_CONSUMER_GROUPS: tl.constexpr,
+) -> None:
+    tl.static_assert(not (FUSE_SCATTER_ADD and USE_TMA_STORE), "Cannot fuse scatter add with TMA store!")
+
+    tidx = tl.program_id(0)
+    dtype: tl.dtype = c_ptr.dtype.element_ty
+
+    m_end_offset = tl.zeros((), dtype=tl.int64)
+    iterated_tiles = tl.zeros((), dtype=tl.int32)
+    for g in tl.range(G):
+        m_size = tl.load(m_sizes + g)
+        if m_size > 0:
+            m_start_offset = m_end_offset
+            m_end_offset = m_start_offset + m_size
+            n_start_offset = g.to(tl.int64) * N
+            n_size = N
+
+            num_m_tiles = tl.cdiv(m_size, BLOCK_SIZE_M)
+            num_n_tiles = tl.cdiv(n_size, BLOCK_SIZE_N)
+            num_tiles = num_m_tiles * num_n_tiles
+
+            if USE_TMA_STORE:
+                c_desc_ptr = tl.make_tensor_descriptor(
+                    c_ptr + m_start_offset * N,
+                    shape=[m_size, n_size],
+                    strides=[n_size, 1],
+                    block_shape=[BLOCK_SIZE_M, BLOCK_SIZE_N],
+                )
+
+            while tidx >= iterated_tiles and tidx < iterated_tiles + num_tiles:
+                gidx = tidx - iterated_tiles
+                tile_m_idx = gidx % num_m_tiles
+                tile_n_idx = gidx // num_m_tiles
+
+                accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+
+                if USE_TMA_LOAD:
+                    tl.static_assert(K % BLOCK_SIZE_K == 0)
+                    m_offset = (m_start_offset + tile_m_idx * BLOCK_SIZE_M).to(tl.int32)
+                    n_offset = (n_start_offset + tile_n_idx * BLOCK_SIZE_N).to(tl.int32)
+                    for k_offset in range(0, K, BLOCK_SIZE_K):
+                        a = tl._experimental_descriptor_load(
+                            a_desc_ptr, [m_offset, k_offset], [BLOCK_SIZE_M, BLOCK_SIZE_K], dtype
+                        )
+                        b = tl._experimental_descriptor_load(
+                            b_desc_ptr, [n_offset, k_offset], [BLOCK_SIZE_N, BLOCK_SIZE_K], dtype
+                        )
+                        if USE_FAST_ACCUM:
+                            accumulator = tl.dot(a, b.T, accumulator)
+                        else:
+                            accumulator += tl.dot(a, b.T)
+                else:
+                    offs_am = tile_m_idx * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+                    offs_bn = tile_n_idx * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+                    offs_k = tl.arange(0, BLOCK_SIZE_K)
+                    a_ptrs = a_desc_ptr + (m_start_offset + offs_am[:, None]) * K + offs_k[None, :]
+                    b_ptrs = b_desc_ptr + (n_start_offset + offs_bn[:, None]) * K + offs_k[None, :]
+                    for k_offset in range(0, K, BLOCK_SIZE_K):
+                        updated_k_offset = k_offset + offs_k
+                        updated_k_offset_mask = updated_k_offset[None, :] < K
+                        a = tl.load(a_ptrs, mask=(offs_am[:, None] < m_size) & updated_k_offset_mask, other=0.0)
+                        b = tl.load(b_ptrs, mask=(offs_bn[:, None] < n_size) & updated_k_offset_mask, other=0.0)
+                        accumulator += tl.dot(a, b.T)
+                        a_ptrs += BLOCK_SIZE_K
+                        b_ptrs += BLOCK_SIZE_K
+
+                if USE_TMA_STORE:
+                    m_offset = (tile_m_idx * BLOCK_SIZE_M).to(tl.int32)
+                    n_offset = (tile_n_idx * BLOCK_SIZE_N).to(tl.int32)
+                    c_desc_ptr.store([m_offset, n_offset], accumulator.to(c_ptr.dtype.element_ty))
+                elif FUSE_SCATTER_ADD:
+                    offs_am = tile_m_idx * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+                    mask = offs_am < m_size
+                    m_offsets = tl.load(scatter_add_indices + m_start_offset + offs_am, mask=mask, cache_modifier=".ca")
+                    offs_bn = tile_n_idx * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+                    c = accumulator.to(c_ptr.dtype.element_ty)
+                    tl.atomic_add(
+                        c_ptr + m_offsets[:, None] * N + offs_bn[None, :],
+                        c,
+                        mask=mask[:, None] & (offs_bn[None, :] < n_size),
+                        sem="relaxed",
+                    )
+                else:
+                    offs_am = tile_m_idx * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+                    offs_bn = tile_n_idx * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+                    c = accumulator.to(c_ptr.dtype.element_ty)
+                    tl.store(
+                        c_ptr + (m_start_offset + offs_am[:, None]) * N + offs_bn[None, :],
+                        c,
+                        mask=(offs_am[:, None] < m_size) & (offs_bn[None, :] < n_size),
+                    )
+                tidx += NUM_SMS
+
+            iterated_tiles += num_tiles
+
+
+def grouped_gemm(
+    x: torch.Tensor,
+    w: torch.Tensor,
+    m_sizes: torch.Tensor,
+    use_fast_accum: bool = True,
+    *,
+    use_warp_specialization: bool = True,
+    output_tensor: Optional[torch.Tensor] = None,
+    scatter_add_indices: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    use_tma_load = not torch.version.hip
+    use_tma_store = False
+
+    if use_tma_load and not HAS_TMA_DESC:
+        use_tma_load = False
+
+    if use_warp_specialization and torch.version.hip:
+        use_warp_specialization = False
+
+    if use_warp_specialization and not _HAS_WS_SUPPORT:
+        use_warp_specialization = False
+
+    if use_warp_specialization:
+        use_tma_store = True
+
+    groups = m_sizes.shape[0]
+
+    assert x.is_contiguous()
+    assert w.is_contiguous()
+    assert m_sizes.is_contiguous()
+
+    m, k = x.shape
+    n = w.shape[0] // groups
+    assert k == w.shape[1]
+
+    if k % 8 != 0 or n % 8 != 0:
+        use_warp_specialization = False
+        use_tma_load = False
+        use_tma_store = False
+        assert output_tensor is None, "Fused scatter add path disabled when K or N is not a multiple of 8."
+
+    if output_tensor is None:
+        fuse_scatter_add = False
+        assert scatter_add_indices is None
+        y = torch.empty((m, n), device=x.device, dtype=torch.bfloat16)
+    else:
+        fuse_scatter_add = True
+        assert scatter_add_indices is not None
+        assert scatter_add_indices.is_contiguous()
+        assert scatter_add_indices.shape == (m,)
+        y = output_tensor
+
+    if m == 0 or n == 0:
+        return y
+
+    num_sms = torch.cuda.get_device_properties("cuda").multi_processor_count
+    desc_helper = None
+    desc_x = x
+    desc_w = w
+
+    if use_tma_load:
+        desc_helper = TmaAutoTuneHelper()
+        desc_helper.init_tma_descriptor("x")
+        desc_helper.init_tma_descriptor("w")
+        desc_x = desc_helper.get_tma_descriptor_kernel_param("x")
+        desc_w = desc_helper.get_tma_descriptor_kernel_param("w")
+
+    if use_tma_store:
+        def alloc_fn(size: int, alignment: int, stream: Optional[int]):
+            return torch.empty(size, device="cuda", dtype=torch.int8)
+
+        triton.set_allocator(alloc_fn)
+
+    def grid(meta):
+        if use_tma_load:
+            desc_helper.fill_2d_tma_descriptor(
+                "x",
+                x.data_ptr(),
+                m,
+                k,
+                meta["BLOCK_SIZE_M"] // meta["NUM_CONSUMER_GROUPS"],
+                meta["BLOCK_SIZE_K"],
+                x.element_size(),
+            )
+            desc_helper.fill_2d_tma_descriptor(
+                "w",
+                w.data_ptr(),
+                n * groups,
+                k,
+                meta["BLOCK_SIZE_N"],
+                meta["BLOCK_SIZE_K"],
+                w.element_size(),
+            )
+        return (num_sms,)
+
+    m_bucket_cap = 16384
+    m_bucket = min(triton.next_power_of_2(m), m_bucket_cap)
+    # Keep the non-warp-specialized path for now; it is enough for the fallback MoE use case.
+    _fbgemm_grouped_gemm[grid](
+        desc_x,
+        desc_w,
+        y,
+        scatter_add_indices,
+        m_sizes,
+        groups,
+        m_bucket,
+        n,
+        k,
+        num_sms,
+        fuse_scatter_add,
+        use_tma_load,
+        use_tma_store,
+        use_fast_accum,
+    )
+    return y
diff --git a/src/kernels/moe.py b/src/kernels/moe.py
new file mode 100644
index 0000000..cbe02c1
--- /dev/null
+++ b/src/kernels/moe.py
@@ -0,0 +1,259 @@
+"""
+Code here all yoinked from https://github.com/pytorch/FBGEMM
+"""
+
+
+from __future__ import annotations
+
+import torch
+from torch.library import custom_op
+import triton
+import triton.language as tl
+
+
+# based on https://github.com/pytorch/FBGEMM/blob/2364886be020b45246eacf8a3eb294194bb08b84/fbgemm_gpu/experimental/gen_ai/src/moe/index_shuffling.cu
+@triton.autotune(
+    configs=[
+        triton.Config({}, num_warps=2),
+        triton.Config({}, num_warps=4),
+        triton.Config({}, num_warps=8),
+    ],
+    key=["n_experts"],
+    reset_to_zero=["token_counts_ptr"],
+)
+@triton.jit
+def _index_shuffling_topk_kernel(
+    scores_ptr,
+    out_experts_ptr,
+    out_token_offsets_ptr,
+    token_counts_ptr,
+    stride_t,
+    stride_e,
+    n_experts,
+    TOP_K: tl.constexpr,
+    BLOCK_E: tl.constexpr,
+):
+    token_idx = tl.program_id(0)
+    expert_offsets = tl.arange(0, BLOCK_E)
+    mask = expert_offsets < n_experts
+    scores = tl.load(
+        scores_ptr + token_idx * stride_t + expert_offsets * stride_e,
+        mask=mask,
+        other=-float("inf"),
+    ).to(tl.float32)
+    expert_ids = expert_offsets.to(tl.int32)
+
+    for k in tl.static_range(TOP_K):
+        _, idx = tl.max(scores, axis=0, return_indices=True, return_indices_tie_break_left=True)
+        offset = token_idx * TOP_K + k
+        token_offset = tl.atomic_add(token_counts_ptr + idx, 1, sem="relaxed")
+        tl.store(out_experts_ptr + offset, idx)
+        tl.store(out_token_offsets_ptr + offset, token_offset)
+        scores = tl.where(expert_ids == idx, -float("inf"), scores)
+
+
+@triton.autotune(
+    configs=[
+        triton.Config({"BLOCK_SIZE": 32}, num_warps=2),
+        triton.Config({"BLOCK_SIZE": 64}, num_warps=2),
+        triton.Config({"BLOCK_SIZE": 128}, num_warps=4),
+    ],
+    key=["n_elements", "n_experts"],
+)
+@triton.jit
+def _index_shuffling_scatter_kernel(
+    expert_indices_ptr,
+    token_offsets_ptr,
+    token_counts_ptr,
+    shuffled_expert_indices_ptr,
+    shuffled_token_indices_ptr,
+    n_elements,
+    n_experts,
+    top_k,
+    BLOCK_E: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+):
+    pid = tl.program_id(0)
+    offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    mask = offsets < n_elements
+
+    expert_indices = tl.load(expert_indices_ptr + offsets, mask=mask, other=0)
+    token_offsets = tl.load(token_offsets_ptr + offsets, mask=mask, other=0)
+
+    expert_offsets = tl.arange(0, BLOCK_E)
+    expert_mask = expert_offsets < n_experts
+    token_counts = tl.load(token_counts_ptr + expert_offsets, mask=expert_mask, other=0)
+    token_starts = tl.sum(
+        tl.where(expert_offsets[None, :] < tl.expand_dims(expert_indices, 1), token_counts[None, :], 0),
+        axis=1,
+    )
+    shuffled_offsets = token_starts + token_offsets
+
+    tl.store(shuffled_expert_indices_ptr + shuffled_offsets, expert_indices, mask=mask)
+    tl.store(shuffled_token_indices_ptr + shuffled_offsets, offsets // top_k, mask=mask)
+
+
+def index_shuffling(scores: torch.Tensor, top_k: int = 1) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    assert scores.ndim == 2
+    assert top_k > 0
+
+    n_tokens, n_experts = scores.shape
+    assert top_k <= n_experts
+    assert scores.is_cuda
+
+    block_e = triton.next_power_of_2(n_experts)
+    assert top_k <= 8
+    assert block_e <= 1024
+
+    n_elements = n_tokens * top_k
+    expert_indices = torch.empty(n_elements, device=scores.device, dtype=torch.int32)
+    token_counts = torch.zeros(n_experts, device=scores.device, dtype=torch.int32)
+    token_offsets = torch.empty(n_elements, device=scores.device, dtype=torch.int32)
+    _index_shuffling_topk_kernel[(n_tokens,)](
+        scores,
+        expert_indices,
+        token_offsets,
+        token_counts,
+        scores.stride(0),
+        scores.stride(1),
+        n_experts,
+        TOP_K=top_k,
+        BLOCK_E=block_e,
+    )
+
+    # can't combine the two kernels cleanly. there needs to be grid wide sync at this point for the
+    # topk kernel to complete its atomics into token_counts. unfortunately triton has no explicit global 
+    # sync primitives, so that's not possible.
+    shuffled_expert_indices = torch.empty(n_elements, device=scores.device, dtype=torch.int32)
+    shuffled_token_indices = torch.empty(n_elements, device=scores.device, dtype=torch.int32)
+
+    def scatter_grid(meta):
+        return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
+
+    _index_shuffling_scatter_kernel[scatter_grid](
+        expert_indices,
+        token_offsets,
+        token_counts,
+        shuffled_expert_indices,
+        shuffled_token_indices,
+        n_elements,
+        n_experts,
+        top_k,
+        BLOCK_E=block_e,
+    )
+
+    return token_counts, shuffled_expert_indices, shuffled_token_indices
+
+
+# yoinked from https://github.com/pytorch/FBGEMM/blob/main/fbgemm_gpu/experimental/gen_ai/gen_ai/moe/gather_scatter.py
+@triton.jit
+def _fbgemm_scatter_add_dense_tokens(
+    out_tokens,
+    in_tokens,
+    token_indices,
+    valid_token_count,
+    D: tl.constexpr,
+    BLOCK_D_OUTER: tl.constexpr,
+    BLOCK_D_INNER: tl.constexpr,
+):
+    input_token_index = tl.program_id(0).to(tl.int64)
+    feature_offset = tl.program_id(1) * BLOCK_D_OUTER + tl.arange(0, BLOCK_D_INNER)[:]
+
+    if valid_token_count is not None:
+        valid_token_count = tl.load(
+            valid_token_count, None, eviction_policy="evict_last"
+        )
+        if input_token_index >= valid_token_count:
+            return
+
+    output_token_index = tl.load(
+        token_indices + input_token_index, None, eviction_policy="evict_last"
+    ).to(tl.int64)
+
+    for _ in range(0, BLOCK_D_OUTER // BLOCK_D_INNER):
+        input_token_value = tl.load(
+            in_tokens + input_token_index * D + feature_offset,
+            None,
+            eviction_policy="evict_first",
+        )
+
+        tl.atomic_add(
+            out_tokens + output_token_index * D + feature_offset,
+            input_token_value,
+            None,
+            sem="relaxed",
+        )
+        feature_offset += BLOCK_D_INNER
+
+# You might notice several things wrong with this function over here but I assure you these are all intentional and have been double checked.
+# First is that the schema here is intentionally wrong, in that the out_tokens is not marked as mutated in-place as is normally required.
+# https://github.com/pytorch/FBGEMM/blob/e8a65d91591033850537c0ff5e5d2bd78685efed/fbgemm_gpu/experimental/gen_ai/gen_ai/moe/gather_scatter.py#L370
+# It might be unintentional, but the registered schema for FBGEMM's `torch.ops.fbgemm.scatter_add_dense_tokens` omits this mutation annotation, and a side
+# effect of doing so is that the final torch.compiled MoE path is about 3x faster. Why? Best guess is that the mutation annotation is causing the 
+# compiler to be more conservative about optimisation around the out_tokens, which inhibits some optimizations. 
+# Secondly, is that it's a torch.library.custom_op instead of a triton_op, which is also intentional because custom_op prevents torch.compile from tracing
+# into the function. If torch.compile traces into the function instead of treating it as an opaque callable, it ends up "optimising" it in a way that regresses performance.
+# In any case, this means that it's a performance loss to be correct, and that this should definitely be tested for regression depending on future torch package changes.
+@custom_op(
+    "world_engine::scatter_add_dense_tokens",
+    schema="(Tensor out_tokens, Tensor in_tokens, Tensor token_indices, Tensor? valid_token_count=None) -> None",
+    mutates_args=(), 
+    device_types="cuda",
+)
+def scatter_add_dense_tokens(
+    out_tokens: torch.Tensor,
+    in_tokens: torch.Tensor,
+    token_indices: torch.Tensor,
+    valid_token_count: torch.Tensor | None = None,
+) -> None:
+    assert torch.version.hip is not None or (
+        torch.version.cuda is not None and torch.version.cuda >= "12.4"
+    ), "Requires CUDA version 12.4 or later on Nvidia GPUs!"
+
+    assert out_tokens.ndim == 2 and in_tokens.ndim == 2
+    assert in_tokens.shape[1] == out_tokens.shape[1]
+    assert token_indices.ndim == 1
+    assert out_tokens.is_cuda and in_tokens.is_cuda and token_indices.is_cuda
+    assert out_tokens.is_contiguous()
+    assert in_tokens.is_contiguous()
+    assert token_indices.is_contiguous()
+    assert out_tokens.dtype in (torch.float16, torch.bfloat16, torch.float32)
+    assert in_tokens.dtype == out_tokens.dtype
+
+    a, D = in_tokens.shape
+    if a == 0:
+        return
+    assert token_indices.shape == (a,)
+
+    NUM_SMS = torch.cuda.get_device_properties("cuda").multi_processor_count
+    if a >= NUM_SMS:
+        BLOCK_D_OUTER = D
+        BLOCK_D_INNER = 1024
+    else:
+        BLOCK_D_OUTER = 512
+        BLOCK_D_INNER = 256
+    while D % BLOCK_D_OUTER != 0:
+        BLOCK_D_OUTER //= 2
+    while D % BLOCK_D_INNER != 0:
+        BLOCK_D_INNER //= 2
+
+    grid = (a, D // BLOCK_D_OUTER)
+    _fbgemm_scatter_add_dense_tokens[grid](
+        out_tokens,
+        in_tokens,
+        token_indices,
+        valid_token_count,
+        D,  # pyre-ignore
+        BLOCK_D_OUTER,  # pyre-ignore
+        BLOCK_D_INNER,  # pyre-ignore
+    )
+    return None
+
+@scatter_add_dense_tokens.register_fake
+def _scatter_add_dense_tokens_fake(
+    out_tokens: torch.Tensor,
+    in_tokens: torch.Tensor,
+    token_indices: torch.Tensor,
+    valid_token_count: torch.Tensor | None = None,
+):
+    return None
diff --git a/src/model/__init__.py b/src/model/__init__.py
index 0dc11e9..1d8860b 100644
--- a/src/model/__init__.py
+++ b/src/model/__init__.py
@@ -1,4 +1,4 @@
-from .world_model import WorldModel, PromptEncoder
+from .world_model import HAS_FBGEMM, WorldModel, PromptEncoder
 from .kv_cache import StaticKVCache
 
-__all__ = ["WorldModel", "StaticKVCache", "PromptEncoder"]
+__all__ = ["HAS_FBGEMM", "WorldModel", "StaticKVCache", "PromptEncoder"]
diff --git a/src/model/world_model.py b/src/model/world_model.py
index 8ea6053..3590924 100644
--- a/src/model/world_model.py
+++ b/src/model/world_model.py
@@ -10,11 +10,17 @@
 import torch.nn.functional as F
 
 
+
 try:
-    from fbgemm_gpu.experimental.gen_ai.moe import index_shuffling
+    from fbgemm_gpu.experimental.gen_ai.moe import index_shuffling as fbgemm_index_shuffling
     import fbgemm_gpu.experimental.gen_ai.moe.gather_scatter  # noqa
+    print("FBGEMM found for MoE kernels! :)")
     HAS_FBGEMM = True
 except ImportError:
+    from ..kernels import index_shuffling as custom_index_shuffling
+    from ..kernels import grouped_gemm as custom_grouped_gemm
+    from ..kernels import scatter_add_dense_tokens as custom_scatter_add_dense_tokens
+    print("FBGEMM not found, using custom kernel implementations :3")
     HAS_FBGEMM = False
 
 
@@ -110,23 +116,22 @@ def forward(self, x: torch.Tensor, gate: torch.Tensor | None = None) -> torch.Te
         logits = self.router(x) if gate is None else gate.reshape(-1, gate.size(-1))
 
         logits_fp32 = logits.float()
-        scores, expert = logits.topk(self.top_k, dim=-1, sorted=False)
-        weights = (scores.float() - logits_fp32.logsumexp(dim=-1, keepdim=True)).exp().to(x.dtype)
+        token_counts, expert_sorted, src = custom_index_shuffling(logits_fp32, top_k=self.top_k)
+
+        E = self.expert_in_proj.size(0)
+        m_sizes = token_counts[:E].to(torch.int32).contiguous()
 
-        expert = expert.flatten()
-        expert_sorted, sort_idx = expert.sort()
-        expert_ids = torch.arange(self.expert_in_proj.size(0), device=expert.device, dtype=expert_sorted.dtype)
-        offsets = torch.searchsorted(expert_sorted, expert_ids, right=True).to(torch.int32)
+        src = src.to(torch.long)
+        expert_sorted = expert_sorted.to(torch.long)
+        logZ = logits_fp32.logsumexp(-1)
+        weights = (logits_fp32[src, expert_sorted] - logZ[src]).exp().to(x.dtype)
 
-        # (1) Pad the *indices* instead of cat-copying x_grouped
-        src = sort_idx // self.top_k
         x_grouped = x.index_select(0, torch.cat((src, src[:1]), dim=0))
-        h = F.grouped_mm(
+        h = custom_grouped_gemm(
             x_grouped,
-            self.expert_in_proj.transpose(-2, -1),
-            offs=offsets
+            self.expert_in_proj.reshape(-1, self.expert_in_proj.shape[-1]).contiguous(),
+            m_sizes,
         )
-        h[-1].zero_()  # ensure last row initialized
 
         if self.config.gated_linear:
             gate_act, up = h.chunk(2, dim=-1)
@@ -134,9 +139,14 @@ def forward(self, x: torch.Tensor, gate: torch.Tensor | None = None) -> torch.Te
         else:
             h = F.silu(h)
 
-        y_grouped = F.grouped_mm(h, self.expert_out_proj.transpose(-2, -1), offs=offsets)[:-1]
-        y = torch.empty_like(y_grouped).index_copy_(0, sort_idx, y_grouped).view(x.size(0), self.top_k, -1)
-        return (y * weights.unsqueeze(-1)).sum(dim=1).reshape(orig_shape)
+        y_grouped = custom_grouped_gemm(
+            h,
+            self.expert_out_proj.reshape(-1, self.expert_out_proj.shape[-1]).contiguous(),
+            m_sizes,
+        )[:-1]
+        out = torch.zeros_like(x)
+        custom_scatter_add_dense_tokens(out, (y_grouped * weights.unsqueeze(-1)).contiguous(), src)
+        return out.reshape(orig_shape)
 
 
 class MoE(nn.Module):
@@ -162,7 +172,7 @@ def forward(self, x: torch.Tensor, gate: torch.Tensor | None = None) -> torch.Te
         logits = self.router(x) if gate is None else gate.reshape(-1, gate.size(-1))
 
         logits32 = logits.float()
-        token_counts, expert_sorted, src = index_shuffling(logits32, top_k=self.top_k)
+        token_counts, expert_sorted, src = fbgemm_index_shuffling(logits32, top_k=self.top_k)
 
         E = self.expert_in_proj.size(0)
         offs = token_counts[:E].cumsum(0).to(torch.int32)
diff --git a/src/world_engine.py b/src/world_engine.py
index 7d1a892..2f991fc 100644
--- a/src/world_engine.py
+++ b/src/world_engine.py
@@ -1,9 +1,10 @@
 from typing import Dict, Optional, Set, Tuple
+from networkx import sigma
 import torch
 from torch import Tensor
 from dataclasses import dataclass, field
 
-from .model import WorldModel, StaticKVCache, PromptEncoder
+from .model import HAS_FBGEMM, WorldModel, StaticKVCache, PromptEncoder
 from .ae import get_ae
 from .patch_model import apply_inference_patches
 from .quantize import quantize_model
@@ -167,15 +168,19 @@ def prep_inputs(self, x, ctrl=None):
             self.set_prompt("An explorable world")
         return {**ctx, **self._prompt_ctx}
 
-    @torch.compile(fullgraph=True, dynamic=False, options=COMPILE_OPTIONS)
     def _denoise_pass(self, x, ctx: Dict[str, Tensor], kv_cache):
         kv_cache.set_frozen(True)
-        sigma = x.new_empty((x.size(0), x.size(1)))
         for step_sig, step_dsig in zip(self.scheduler_sigmas, self.scheduler_sigmas.diff()):
-            v = self.model(x, sigma.fill_(step_sig), **ctx, kv_cache=kv_cache)
+            v = self._denoise_step(x, step_sig, step_dsig, ctx, kv_cache)
             x = x + step_dsig * v
         return x
 
+    @torch.compile(fullgraph=True, dynamic=False, options=COMPILE_OPTIONS)
+    def _denoise_step(self, x, step_sig, step_dsig, ctx: Dict[str, Tensor], kv_cache):
+        sigma = x.new_empty((x.size(0), x.size(1)))
+        sigma.fill_(step_sig)
+        return self.model(x, sigma, **ctx, kv_cache=kv_cache)
+
     @torch.compile(fullgraph=True, dynamic=False, options=COMPILE_OPTIONS)
     def _cache_pass(self, x, ctx: Dict[str, Tensor], kv_cache):
         """Side effect: updates kv cache"""
diff --git a/tests/benchmark_moe.py b/tests/benchmark_moe.py
new file mode 100644
index 0000000..dc54f31
--- /dev/null
+++ b/tests/benchmark_moe.py
@@ -0,0 +1,352 @@
+import pytest
+import torch
+from time import perf_counter
+from pathlib import Path
+
+from world_engine import WorldEngine
+
+
+def _set_seed(seed: int) -> None:
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+
+
+def _frame_debug_summary(frame: torch.Tensor) -> str:
+    flat = frame.detach().float().reshape(-1)
+    head = flat[:8].cpu().tolist()
+    head_fmt = ", ".join(f"{v:.4f}" for v in head)
+    return (
+        f"shape={tuple(frame.shape)} "
+        f"head=[{head_fmt}] "
+        f"mean={flat.mean().item():.6f} "
+        f"std={flat.std().item():.6f}"
+    )
+
+
+def prewarm_custom_moe_kernels(engine: WorldEngine) -> None:
+    from world_engine.kernels import index_shuffling, scatter_add_dense_tokens
+    from world_engine.model.world_model import HAS_FBGEMM
+
+    cfg = engine.model_cfg
+    if HAS_FBGEMM or not getattr(cfg, "moe", False):
+        return
+
+    n_tokens = cfg.tokens_per_frame
+    n_experts = cfg.moe_n_experts
+    top_k = min(cfg.moe_top_k, n_experts)
+    d_model = cfg.d_model
+
+    scores = torch.randn((n_tokens, n_experts), device=engine.device, dtype=torch.float32)
+    _, _, src = index_shuffling(scores, top_k=top_k)
+
+    out_tokens = torch.zeros((n_tokens, d_model), device=engine.device, dtype=engine.dtype)
+    in_tokens = torch.randn((n_tokens * top_k, d_model), device=engine.device, dtype=engine.dtype)
+    scatter_add_dense_tokens(out_tokens, in_tokens, src)
+    torch.cuda.synchronize()
+
+
+def log_moe_runtime_path(engine: WorldEngine) -> None:
+    from world_engine.model import world_model as world_model_module
+
+    has_fbgemm = world_model_module.HAS_FBGEMM
+
+    mlp_types = sorted(
+        {
+            type(block.mlp).__name__
+            for block in engine.model.transformer.blocks
+            if hasattr(block, "mlp")
+        }
+    )
+    print(
+        "moe_runtime_path "
+        f"HAS_FBGEMM={has_fbgemm} "
+        f"moe={getattr(engine.model_cfg, 'moe', False)} "
+        f"moe_n_experts={getattr(engine.model_cfg, 'moe_n_experts', None)} "
+        f"moe_top_k={getattr(engine.model_cfg, 'moe_top_k', None)} "
+        f"mlp_types={mlp_types} "
+        f"fbgemm_index_shuffling={getattr(world_model_module, 'fbgemm_index_shuffling', None)!r} "
+        f"custom_index_shuffling={getattr(world_model_module, 'custom_index_shuffling', None)!r}"
+    )
+
+
+def version_with_commit(pkg):
+    import json
+    from importlib.metadata import distribution
+    dist = distribution(pkg.__name__.split('.')[0])
+    version = dist.version
+    try:
+        data = dist.read_text("direct_url.json")
+        commit = (data and json.loads(data).get("vcs_info", {}).get("commit_id"))
+    except (FileNotFoundError, json.JSONDecodeError, TypeError):
+        commit = None
+    return f"{version} @ {commit[:7]}" if commit else version
+
+
+@pytest.fixture(scope="session", autouse=False)
+def print_env_info():
+    import platform
+    import world_engine as world_engine_pkg
+    print(
+        "\n=== Environment ===\n"
+        f"torch:        {torch.__version__}\n"
+        f"torch.cuda:   {torch.version.cuda}\n"
+        f"world_engine: {version_with_commit(world_engine_pkg)}\n\n"
+        "=== Hardware ===\n"
+        f"OS:   {platform.system()} {platform.release()} ({platform.machine()})\n"
+        f"CPU:  {platform.processor() or 'unknown'}"
+    )
+
+    if torch.cuda.is_available():
+        idx = torch.cuda.current_device()
+        props = torch.cuda.get_device_properties(idx)
+        print(
+            f"GPU:  {props.name}\n"
+            f"      capability {props.major}.{props.minor}\n"
+            f"      total memory: {props.total_memory / 1e9:.1f} GB"
+        )
+    else:
+        print("GPU:  none (CUDA not available)")
+
+
+@pytest.fixture(scope="session", autouse=True)
+def _compact_benchmark_table(request):
+    request.config._benchmarksession.columns = ["median", "max", "mean", "stddev", "rounds", "iterations"]
+
+    import pytest_benchmark.session as pb_session
+    import pytest_benchmark.table as pb_table
+
+    if not getattr(pb_session, "_vram_cols", False):
+        orig = pb_session.BenchmarkSession.prepare_benchmarks
+
+        def prepare_benchmarks(self):
+            for bench in orig(self):
+                info = bench.get("extra_info") or {}
+                if "max_vram_alloc" in info:
+                    bench["rounds"] = info["max_vram_alloc"]
+                if "max_vram_reserved" in info:
+                    bench["iterations"] = info["max_vram_reserved"]
+                yield bench
+
+        pb_session.BenchmarkSession.prepare_benchmarks = prepare_benchmarks
+        pb_session._vram_cols = True
+
+    if not getattr(pb_table, "_vram_labels", False):
+        code = pb_table.TableResults.display.__code__
+        mapping = {"Rounds": "max_vram_alloc", "Iterations": "max_vram_reserved"}
+        pb_table.TableResults.display.__code__ = code.replace(
+            co_consts=tuple(mapping.get(c, c) for c in code.co_consts)
+        )
+        pb_table._vram_labels = True
+
+
+def get_warm_engine(model_uri, model_overrides=None):
+    model_config_overrides = {"ae_uri": "Overworld-Models/taehv1_5", "ae_type": "taehv"}
+    model_config_overrides.update(model_overrides or {})
+    engine = WorldEngine(
+        model_uri,
+        model_config_overrides=model_config_overrides,
+        device="cuda",
+        load_weights=False
+    )
+
+    log_moe_runtime_path(engine)
+
+    # prewarm_custom_moe_kernels(engine)
+
+    # global warmup
+    for _ in range(3):
+        engine.gen_frame()
+    return engine
+
+
+@pytest.fixture(scope="session")
+def engine(model_uri="Overworld-Models/Lapp0-WP-Mini-1.4.5-BL-Distill"):
+    return get_warm_engine(model_uri)
+
+@pytest.fixture(scope="session")
+def last_latent(engine):
+    return engine.gen_frame(return_img=False).detach()
+
+
+
+def test_img_decoder_only(benchmark, engine, last_latent):
+    def run():
+        with torch.amp.autocast("cuda", torch.bfloat16):
+            engine.vae.decode(last_latent)
+        torch.cuda.synchronize()
+
+    benchmark(run)
+
+
+default_cfg = {
+    "taehv_ae": True,
+    "ae_uri": "Overworld-Models/taehv1_5",
+    "gated_linear": False,
+    #"ctrl_conditioning_period": None,
+    #"scheduler_sigmas": [1.0, 0.75, 0.5, 0.25, 0.0],
+    #"channels": 16,
+    #"gated_linear": False,
+    #"ctrl_conditioning_period": 3,
+    #"prompt_conditioning": None,
+    #"global_window": 256,
+    "n_kv_heads": 16,
+    "n_heads": 32,
+}
+dense_moe = {**default_cfg, "moe": True, "moe_n_experts": 8, "moe_top_k": 8, "shared_frame_experts": False}
+xs_moe = {**default_cfg, "moe": True, "moe_n_experts": 16, "moe_top_k": 8, "shared_frame_experts": False, "n_layers": 8}
+sparse_moe = {**default_cfg, "moe": True, "moe_n_experts": 16, "moe_top_k": 1, "shared_frame_experts": False}
+target_moe = {**default_cfg, "moe": True, "moe_n_experts": 32, "moe_top_k": 4, "shared_frame_experts": False}
+
+moes = [dense_moe, sparse_moe, target_moe]
+shared_frame_moes = [{**x, "shared_frame_experts": True} for x in moes]
+
+MODEL_OVERRIDES = [
+    default_cfg,
+    *moes,
+    #*shared_frame_moes,
+]
+MOE_MODEL_OVERRIDES = [cfg for cfg in MODEL_OVERRIDES if cfg.get("moe", False)]
+DENSE_MOE_MODEL_OVERRIDES = [dense_moe]
+XS_MOE_MODEL_OVERRIDES = [xs_moe]
+
+@pytest.mark.parametrize(
+    "model_overrides", XS_MOE_MODEL_OVERRIDES,
+    ids=lambda d: ",".join(f"{k}={v}" for k, v in d.items())
+)
+def test_moe_gen_frame_once(model_overrides):
+    engine = WorldEngine(
+        "Overworld-Models/Lapp0-WP-Mini-1.4.5-BL-Distill",
+        model_config_overrides=model_overrides,
+        device="cuda",
+        load_weights=False,
+    )
+    prewarm_custom_moe_kernels(engine)
+    frame = engine.gen_frame(return_img=False)
+    torch.cuda.synchronize()
+
+    assert frame is not None
+    assert frame.is_cuda
+    assert frame.dtype == engine.dtype
+
+
+@pytest.mark.parametrize(
+    "model_overrides", XS_MOE_MODEL_OVERRIDES,
+    ids=lambda d: ",".join(f"{k}={v}" for k, v in d.items())
+)
+def test_moe_gen_frame_rollout_repro(model_overrides):
+    _set_seed(0)
+    warm_start = perf_counter()
+    engine = get_warm_engine(
+        "Overworld-Models/Lapp0-WP-Mini-1.4.5-BL-Distill",
+        model_overrides=model_overrides,
+    )
+    warm_ms = (perf_counter() - warm_start) * 1000
+
+    frame = None
+    print(f"rollout_repro warmup_ms={warm_ms:.2f}")
+    for _ in range(3):
+        setup_start = perf_counter()
+        engine.reset()
+        engine.gen_frame(return_img=False)
+        torch.cuda.synchronize()
+        setup_ms = (perf_counter() - setup_start) * 1000
+
+        rollout_start = perf_counter()
+        for _ in range(64):
+            frame = engine.gen_frame(return_img=False)
+        torch.cuda.synchronize()
+        rollout_ms = (perf_counter() - rollout_start) * 1000
+        print(f"rollout_repro setup_ms={setup_ms:.2f} rollout_ms={rollout_ms:.2f} per_frame_ms={rollout_ms / 64:.2f}")
+        print(f"rollout_repro output {_frame_debug_summary(frame)}")
+
+    assert frame is not None
+    assert frame.is_cuda
+
+
+@pytest.mark.parametrize(
+    "model_overrides", DENSE_MOE_MODEL_OVERRIDES,
+    ids=lambda d: ",".join(f"{k}={v}" for k, v in d.items())
+)
+def test_dump_moe_fbgemm_fx_graph(model_overrides):
+    from torch._dynamo import export
+
+    engine = WorldEngine(
+        "Overworld-Models/Lapp0-WP-Mini-1.4.5-BL-Distill",
+        model_config_overrides={"ae_uri": "Overworld-Models/taehv1_5", "ae_type": "taehv", **model_overrides},
+        device="cuda",
+        load_weights=False,
+    )
+
+    class SingleDenoiseStep(torch.nn.Module):
+        def __init__(self, engine: WorldEngine):
+            super().__init__()
+            self.engine = engine
+
+        def forward(self, x):
+            ctx = self.engine.prep_inputs(x)
+            self.engine.kv_cache.set_frozen(True)
+            sigma = x.new_empty((x.size(0), x.size(1)))
+            step_sig = self.engine.scheduler_sigmas[0]
+            return self.engine.model(x, sigma.fill_(step_sig), **ctx, kv_cache=self.engine.kv_cache)
+
+    x = torch.randn(engine.frm_shape, device=engine.device, dtype=engine.dtype)
+    module = SingleDenoiseStep(engine).eval()
+    with torch.no_grad():
+        exported, guards = export(module, x)
+
+    out = Path("tests/generated_kernels/fbgemm_moe_fx_graph.txt")
+    out.write_text(
+        "\n".join(
+            [
+                f"guards={guards}",
+                "",
+                "=== graph ===",
+                str(exported.graph),
+                "",
+                "=== code ===",
+                exported.code,
+            ]
+        )
+    )
+    print(f"wrote FX graph to {out}")
+
+
+@pytest.mark.parametrize("dit_only", [True])
+@pytest.mark.parametrize("n_frames", [256])
+@pytest.mark.parametrize(
+    "model_overrides", XS_MOE_MODEL_OVERRIDES,
+    ids=lambda d: (",".join(f"{k}={v}" for k, v in d.items()) or "") if d else ""
+)
+def test_ar_rollout(benchmark, dit_only, n_frames, model_overrides):
+    import gc
+
+    gc.collect()
+    torch.cuda.empty_cache()
+    engine = get_warm_engine("Overworld-Models/Lapp0-WP-Mini-1.4.5-BL-Distill", model_overrides=model_overrides)
+    total_params = sum(p.numel() for p in engine.model.parameters())
+    active_params = int(engine.model.get_active_parameters())
+    benchmark.name = f"{benchmark.name} | params={total_params:,} | active={active_params:,}"
+
+    max_alloc = 0
+    max_reserved = 0
+
+    def setup():
+        engine.reset()
+        engine.gen_frame(return_img=not dit_only)
+        torch.cuda.synchronize()
+        torch.cuda.reset_peak_memory_stats()
+
+    def target():
+        for _ in range(n_frames):
+            engine.gen_frame(return_img=not dit_only)
+        torch.cuda.synchronize()
+
+    def teardown():
+        nonlocal max_alloc, max_reserved
+        max_alloc = max(max_alloc, torch.cuda.max_memory_allocated())
+        max_reserved = max(max_reserved, torch.cuda.max_memory_reserved())
+
+    benchmark.pedantic(target, setup=setup, teardown=teardown, rounds=10)
+    benchmark.extra_info["max_vram_alloc"] = round(max_alloc / 2**30, 2)
+    benchmark.extra_info["max_vram_reserved"] = round(max_reserved / 2**30, 2)
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..dd88c26
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,7 @@
+def pytest_addoption(parser):
+    parser.addoption(
+        "--run-kernel-benchmarks",
+        action="store_true",
+        default=False,
+        help="run opt-in MoE kernel timing benchmarks",
+    )
diff --git a/tests/test_moe_kernels.py b/tests/test_moe_kernels.py
new file mode 100644
index 0000000..eb9859b
--- /dev/null
+++ b/tests/test_moe_kernels.py
@@ -0,0 +1,881 @@
+from __future__ import annotations
+
+from collections.abc import Callable
+from pathlib import Path
+from types import SimpleNamespace
+import statistics
+import sys
+import time
+
+import pytest
+import torch
+import torch.nn.functional as F
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+try:
+    from src.kernels import (
+        grouped_gemm,
+        index_shuffling,
+        scatter_add_dense_tokens,
+    )
+    HAS_LOCAL_KERNELS = True
+except ImportError:
+    grouped_gemm = None
+    index_shuffling = None
+    scatter_add_dense_tokens = None
+    HAS_LOCAL_KERNELS = False
+import src.model.world_model as world_model_module
+from src.model.world_model import MoE, MoEWithoutFBGEMM
+
+
+CUDA_ONLY = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is required for MoE kernel tests")
+LOCAL_KERNELS_ONLY = pytest.mark.skipif(not HAS_LOCAL_KERNELS, reason="local kernels package is not present on this branch")
+
+COMPILE_OPTIONS = {
+    "max_autotune": True,
+    "coordinate_descent_tuning": True,
+    "triton.cudagraphs": True,
+}
+
+# fbgemm kernels
+def _load_fbgemm_index_shuffling() -> Callable | None:
+    try:
+        from fbgemm_gpu.experimental.gen_ai.moe import index_shuffling as fbgemm_index_shuffling
+    except ImportError:
+        return None
+    return fbgemm_index_shuffling
+
+
+def _has_fbgemm_scatter_add() -> bool:
+    return hasattr(torch.ops, "fbgemm") and hasattr(torch.ops.fbgemm, "scatter_add_dense_tokens")
+
+
+def _load_fbgemm_grouped_gemm() -> Callable | None:
+    try:
+        from fbgemm_gpu.experimental.gemm.triton_gemm.grouped_gemm import grouped_gemm as fbgemm_grouped_gemm
+    except ImportError:
+        return None
+    return fbgemm_grouped_gemm
+
+
+def has_fbgemm_moe_kernels() -> bool:
+    return _load_fbgemm_index_shuffling() is not None and _has_fbgemm_scatter_add()
+
+
+def fbgemm_index_shuffling(scores: torch.Tensor, top_k: int = 1) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    op = _load_fbgemm_index_shuffling()
+    if op is None:
+        raise RuntimeError("fbgemm index_shuffling is not available in this environment")
+    return op(scores, top_k=top_k)
+
+
+def fbgemm_scatter_add_dense_tokens(
+    out_tokens: torch.Tensor,
+    in_tokens: torch.Tensor,
+    token_indices: torch.Tensor,
+    valid_token_count: torch.Tensor | None = None,
+) -> None:
+    if not _has_fbgemm_scatter_add():
+        raise RuntimeError("fbgemm scatter_add_dense_tokens is not available in this environment")
+    torch.ops.fbgemm.scatter_add_dense_tokens(out_tokens, in_tokens, token_indices, valid_token_count)
+
+
+def fbgemm_grouped_gemm(
+    x: torch.Tensor,
+    w: torch.Tensor,
+    m_sizes: torch.Tensor,
+    use_fast_accum: bool = True,
+) -> torch.Tensor:
+    op = _load_fbgemm_grouped_gemm()
+    if op is None:
+        raise RuntimeError("fbgemm grouped_gemm is not available in this environment")
+    return op(x, w, m_sizes, use_fast_accum=use_fast_accum)
+
+
+def _canonical_pairs(expert_indices: torch.Tensor, token_indices: torch.Tensor, n_tokens: int) -> torch.Tensor:
+    key = expert_indices.to(torch.int64) * n_tokens + token_indices.to(torch.int64)
+    return torch.stack((expert_indices, token_indices), dim=1).index_select(0, torch.argsort(key))
+
+
+def _random_scores(n_tokens: int, n_experts: int, device: str) -> torch.Tensor:
+    return torch.randn(n_tokens, n_experts, device=device, dtype=torch.float32)
+
+
+def _max_pair_diff(lhs: torch.Tensor, rhs: torch.Tensor) -> int:
+    if lhs.numel() == 0:
+        return 0
+    return int((lhs.to(torch.int64) - rhs.to(torch.int64)).abs().max().item())
+
+
+def _time_cuda_ms(fn, warmup: int = 50, iters: int = 250) -> float:
+    stream = torch.cuda.Stream()
+    start = torch.cuda.Event(enable_timing=True)
+    end = torch.cuda.Event(enable_timing=True)
+    samples = []
+    with torch.cuda.stream(stream):
+        for _ in range(warmup):
+            fn()
+        for _ in range(iters):
+            start.record(stream)
+            fn()
+            end.record(stream)
+            end.synchronize()
+            samples.append(start.elapsed_time(end))
+    return statistics.median(samples)
+
+
+def _make_grouped_gemm_inputs(
+    n_experts: int,
+    d_model: int,
+    d_hidden: int,
+    *,
+    top_k: int = 8,
+    n_tokens: int = 512,
+    dtype: torch.dtype = torch.bfloat16,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    counts = torch.full((n_experts,), n_tokens * top_k // n_experts, device="cuda", dtype=torch.int32)
+    counts[-1] += n_tokens * top_k - int(counts.sum().item())
+    x_grouped = torch.randn((n_tokens * top_k + 1, d_model), device="cuda", dtype=dtype).contiguous()
+    w = torch.randn((n_experts * d_hidden, d_model), device="cuda", dtype=dtype).contiguous()
+    return x_grouped, w, counts.contiguous()
+
+
+def _grouped_gemm_weight_for_torch(
+    w: torch.Tensor,
+    n_experts: int,
+    d_hidden: int,
+) -> torch.Tensor:
+    return w.view(n_experts, d_hidden, w.shape[1]).transpose(-2, -1).contiguous()
+
+
+def _grouped_gemm_offs(m_sizes: torch.Tensor) -> torch.Tensor:
+    return m_sizes.cumsum(0).to(torch.int32).contiguous()
+
+
+def _make_moe_inputs(
+    *,
+    n_tokens: int = 512,
+    n_experts: int = 16,
+    top_k: int = 8,
+    d_model: int = 2048,
+    d_hidden: int = 1024,
+    dtype: torch.dtype = torch.bfloat16,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    x = torch.randn((n_tokens, d_model), device="cuda", dtype=dtype).contiguous()
+    logits = torch.randn((n_tokens, n_experts), device="cuda", dtype=torch.float32).contiguous()
+    expert_in_proj = torch.randn((n_experts, d_hidden, d_model), device="cuda", dtype=dtype).contiguous()
+    expert_out_proj = torch.randn((n_experts, d_model, d_hidden), device="cuda", dtype=dtype).contiguous()
+    return x, logits, expert_in_proj, expert_out_proj
+
+
+def _make_moe_config(
+    *,
+    n_experts: int = 16,
+    top_k: int = 8,
+    d_model: int = 2048,
+    d_hidden: int = 1024,
+    gated_linear: bool = False,
+):
+    mlp_ratio = (d_hidden * top_k) / d_model
+    return SimpleNamespace(
+        moe_top_k=top_k,
+        moe_n_experts=n_experts,
+        d_model=d_model,
+        mlp_ratio=mlp_ratio,
+        gated_linear=gated_linear,
+    )
+
+
+def _make_moe_modules(
+    *,
+    n_experts: int = 16,
+    top_k: int = 8,
+    d_model: int = 2048,
+    d_hidden: int = 1024,
+    gated_linear: bool = False,
+    dtype: torch.dtype = torch.bfloat16,
+):
+    cfg = _make_moe_config(
+        n_experts=n_experts,
+        top_k=top_k,
+        d_model=d_model,
+        d_hidden=d_hidden,
+        gated_linear=gated_linear,
+    )
+    moe_fbgemm = MoE(cfg).eval().to(device="cuda", dtype=dtype)
+    moe_fallback = MoEWithoutFBGEMM(cfg).eval().to(device="cuda", dtype=dtype)
+    moe_fallback.load_state_dict(moe_fbgemm.state_dict())
+    return moe_fbgemm, moe_fallback
+
+
+def _run_moe_module_eager(
+    module: torch.nn.Module,
+    x: torch.Tensor,
+    gate: torch.Tensor | None = None,
+) -> torch.Tensor:
+    with torch.inference_mode():
+        return module(x, gate=gate)
+
+
+def _run_custom_moe_eager(
+    x: torch.Tensor,
+    logits: torch.Tensor,
+    expert_in_proj: torch.Tensor,
+    expert_out_proj: torch.Tensor,
+    top_k: int,
+) -> torch.Tensor:
+    logits_fp32 = logits.float()
+    token_counts, expert_sorted, src = index_shuffling(logits_fp32, top_k=top_k)
+    m_sizes = token_counts[: expert_in_proj.shape[0]].to(torch.int32).contiguous()
+    src = src.to(torch.long)
+    expert_sorted = expert_sorted.to(torch.long)
+    log_z = logits_fp32.logsumexp(-1)
+    weights = (logits_fp32[src, expert_sorted] - log_z[src]).exp().to(x.dtype)
+
+    x_grouped = x.index_select(0, torch.cat((src, src[:1]), dim=0))
+    h = grouped_gemm(
+        x_grouped,
+        expert_in_proj.reshape(-1, expert_in_proj.shape[-1]).contiguous(),
+        m_sizes,
+    )
+    h = F.silu(h)
+    y_grouped = grouped_gemm(
+        h,
+        expert_out_proj.reshape(-1, expert_out_proj.shape[-1]).contiguous(),
+        m_sizes,
+    )[:-1]
+    out = torch.zeros_like(x)
+    scatter_add_dense_tokens(out, (y_grouped * weights.unsqueeze(-1)).contiguous(), src)
+    return out
+
+
+def _run_fbgemm_moe_eager(
+    x: torch.Tensor,
+    logits: torch.Tensor,
+    expert_in_proj: torch.Tensor,
+    expert_out_proj: torch.Tensor,
+    top_k: int,
+) -> torch.Tensor:
+    logits_fp32 = logits.float()
+    token_counts, expert_sorted, src = fbgemm_index_shuffling(logits_fp32, top_k=top_k)
+    offs = token_counts[: expert_in_proj.shape[0]].cumsum(0).to(torch.int32)
+    src = src.to(torch.long)
+    expert_sorted = expert_sorted.to(torch.long)
+    log_z = logits_fp32.logsumexp(-1)
+    weights = (logits_fp32[src, expert_sorted] - log_z[src]).exp().to(x.dtype)
+
+    x_grouped = x.index_select(0, torch.cat((src, src[:1]), dim=0))
+    h = F.grouped_mm(x_grouped, expert_in_proj.transpose(-2, -1), offs=offs)
+    h = F.silu(h)
+    y_grouped = F.grouped_mm(h, expert_out_proj.transpose(-2, -1), offs=offs)[:-1]
+    out = torch.zeros_like(x)
+    fbgemm_scatter_add_dense_tokens(out, (y_grouped * weights.unsqueeze(-1)).contiguous(), src)
+    return out
+
+
+def _run_loaded_moe_eager(
+    x: torch.Tensor,
+    logits: torch.Tensor,
+    expert_in_proj: torch.Tensor,
+    expert_out_proj: torch.Tensor,
+    top_k: int,
+) -> torch.Tensor:
+    logits_fp32 = logits.float()
+    token_counts, expert_sorted, src = world_model_module.fbgemm_index_shuffling(logits_fp32, top_k=top_k)
+    offs = token_counts[: expert_in_proj.shape[0]].cumsum(0).to(torch.int32)
+    src = src.to(torch.long)
+    expert_sorted = expert_sorted.to(torch.long)
+    log_z = logits_fp32.logsumexp(-1)
+    weights = (logits_fp32[src, expert_sorted] - log_z[src]).exp().to(x.dtype)
+
+    x_grouped = x.index_select(0, torch.cat((src, src[:1]), dim=0))
+    h = F.grouped_mm(x_grouped, expert_in_proj.transpose(-2, -1), offs=offs)
+    h = F.silu(h)
+    y_grouped = F.grouped_mm(h, expert_out_proj.transpose(-2, -1), offs=offs)[:-1]
+    out = torch.zeros_like(x)
+    torch.ops.fbgemm.scatter_add_dense_tokens(out, (y_grouped * weights.unsqueeze(-1)).contiguous(), src)
+    return out
+
+
+@torch.compile(fullgraph=True, dynamic=False, options=COMPILE_OPTIONS)
+def _run_moe_module_compiled(moe: MoE, x: torch.Tensor) -> torch.Tensor:
+    return moe(x)
+
+
+def _run_moe_module_compiled_inference(moe: MoE, x: torch.Tensor) -> torch.Tensor:
+    with torch.inference_mode():
+        return _run_moe_module_compiled(moe, x).clone()
+
+
+@torch.compile(fullgraph=True, dynamic=False, options=COMPILE_OPTIONS)
+def _run_moe_fallback_module_compiled(moe: MoEWithoutFBGEMM, x: torch.Tensor) -> torch.Tensor:
+    return moe(x)
+
+
+def _run_moe_fallback_module_compiled_inference(moe: MoEWithoutFBGEMM, x: torch.Tensor) -> torch.Tensor:
+    with torch.inference_mode():
+        return _run_moe_fallback_module_compiled(moe, x).clone()
+
+def _require_benchmark_flag(request) -> None:
+    if not request.config.getoption("--run-kernel-benchmarks"):
+        pytest.skip("pass --run-kernel-benchmarks to run kernel timing tests")
+
+
+@CUDA_ONLY
+@LOCAL_KERNELS_ONLY
+@pytest.mark.parametrize(("n_tokens", "n_experts", "top_k"), [(512, 32, 2), (2048, 64, 4)])
+def test_index_shuffling_matches_topk_semantics(n_tokens: int, n_experts: int, top_k: int):
+    scores = _random_scores(n_tokens, n_experts, "cuda")
+    token_counts, expert_indices, token_indices = index_shuffling(scores, top_k=top_k)
+
+    _, expected_experts = scores.topk(top_k, dim=-1, sorted=False)
+    expected_pairs = _canonical_pairs(
+        expected_experts.reshape(-1),
+        torch.arange(n_tokens, device=scores.device, dtype=torch.int64).repeat_interleave(top_k),
+        n_tokens=n_tokens,
+    )
+
+    actual_pairs = _canonical_pairs(expert_indices, token_indices, n_tokens=n_tokens)
+    expected_counts = torch.bincount(expected_pairs[:, 0], minlength=n_experts)
+
+    assert torch.equal(token_counts.cpu(), expected_counts.cpu())
+    assert torch.equal(actual_pairs.cpu(), expected_pairs.cpu())
+
+
+@CUDA_ONLY
+@LOCAL_KERNELS_ONLY
+@pytest.mark.skipif(not has_fbgemm_moe_kernels(), reason="fbgemm MoE kernels are not available")
+@pytest.mark.parametrize(("n_tokens", "n_experts", "top_k"), [(512, 32, 2), (2048, 128, 4)])
+def test_index_shuffling_matches_fbgemm_groups(n_tokens: int, n_experts: int, top_k: int):
+    scores = _random_scores(n_tokens, n_experts, "cuda")
+    ref_counts, ref_experts, ref_tokens = fbgemm_index_shuffling(scores, top_k=top_k)
+    test_counts, test_experts, test_tokens = index_shuffling(scores, top_k=top_k)
+
+    ref_pairs = _canonical_pairs(ref_experts, ref_tokens, n_tokens=n_tokens)
+    test_pairs = _canonical_pairs(test_experts, test_tokens, n_tokens=n_tokens)
+    counts_diff = _max_pair_diff(test_counts[:n_experts], ref_counts[:n_experts])
+    expert_diff = _max_pair_diff(test_pairs[:, 0], ref_pairs[:, 0])
+    token_diff = _max_pair_diff(test_pairs[:, 1], ref_pairs[:, 1])
+
+    print(
+        f"\nindex_shuffling diffs: "
+        f"max_count_diff={counts_diff} "
+        f"max_expert_diff={expert_diff} "
+        f"max_token_diff={token_diff}"
+    )
+
+    assert torch.equal(test_counts[:n_experts].cpu(), ref_counts[:n_experts].cpu())
+    assert torch.equal(test_pairs.cpu(), ref_pairs.cpu())
+
+
+@CUDA_ONLY
+@LOCAL_KERNELS_ONLY
+@pytest.mark.parametrize(("n_out", "n_in", "width"), [(1024, 2048, 256), (2048, 4096, 512)])
+def test_scatter_add_dense_tokens_matches_reference(n_out: int, n_in: int, width: int):
+    in_tokens = torch.randn(n_in, width, device="cuda", dtype=torch.bfloat16)
+    token_indices = torch.randint(0, n_out, (n_in,), device="cuda", dtype=torch.int64)
+
+    expected = torch.zeros(n_out, width, device="cuda", dtype=torch.float32)
+    actual = torch.zeros(n_out, width, device="cuda", dtype=in_tokens.dtype)
+
+    expected.index_add_(0, token_indices, in_tokens.float())
+    scatter_add_dense_tokens(actual, in_tokens, token_indices)
+
+    torch.testing.assert_close(actual.float(), expected, atol=5e-2, rtol=5e-2)
+
+
+@CUDA_ONLY
+@LOCAL_KERNELS_ONLY
+@pytest.mark.skipif(not has_fbgemm_moe_kernels(), reason="fbgemm MoE kernels are not available")
+@pytest.mark.parametrize(("n_out", "n_in", "width"), [(1024, 2048, 256), (2048, 4096, 512)])
+def test_scatter_add_dense_tokens_matches_fbgemm(n_out: int, n_in: int, width: int):
+    in_tokens = torch.randn(n_in, width, device="cuda", dtype=torch.bfloat16)
+    token_indices = torch.randint(0, n_out, (n_in,), device="cuda", dtype=torch.int64)
+
+    ref = torch.zeros(n_out, width, device="cuda", dtype=in_tokens.dtype)
+    actual = torch.zeros_like(ref)
+
+    fbgemm_scatter_add_dense_tokens(ref, in_tokens.contiguous(), token_indices.contiguous())
+    scatter_add_dense_tokens(actual, in_tokens, token_indices)
+    diff = (actual.float() - ref.float()).abs()
+    max_abs_diff = float(diff.max().item())
+    mean_abs_diff = float(diff.mean().item())
+
+    print(
+        f"\nscatter_add_dense_tokens diffs: "
+        f"max_abs_diff={max_abs_diff:.6f} "
+        f"mean_abs_diff={mean_abs_diff:.6f}"
+    )
+
+    torch.testing.assert_close(actual.float(), ref.float(), atol=5e-2, rtol=5e-2)
+
+
+@CUDA_ONLY
+@LOCAL_KERNELS_ONLY
+@pytest.mark.skipif(_load_fbgemm_grouped_gemm() is None, reason="fbgemm grouped_gemm is not available")
+@pytest.mark.parametrize(("n_experts", "d_model", "d_hidden"), [(8, 2048, 1024), (16, 2048, 1024)])
+def test_grouped_gemm_matches_fbgemm(n_experts: int, d_model: int, d_hidden: int):
+    x_grouped, w, m_sizes = _make_grouped_gemm_inputs(n_experts, d_model, d_hidden)
+    ref = fbgemm_grouped_gemm(x_grouped, w, m_sizes)
+    actual = grouped_gemm(x_grouped, w, m_sizes)
+    diff = (actual.float() - ref.float()).abs()
+    max_abs_diff = float(diff.max().item())
+    mean_abs_diff = float(diff.mean().item())
+
+    print(
+        f"\ngrouped_gemm diffs: "
+        f"max_abs_diff={max_abs_diff:.6f} "
+        f"mean_abs_diff={mean_abs_diff:.6f}"
+    )
+
+    torch.testing.assert_close(actual.float(), ref.float(), atol=5e-2, rtol=5e-2)
+
+
+@CUDA_ONLY
+@LOCAL_KERNELS_ONLY
+@pytest.mark.parametrize(("n_experts", "d_model", "d_hidden"), [(8, 2048, 1024), (16, 2048, 1024)])
+def test_grouped_gemm_eager_vs_torch_grouped_mm(n_experts: int, d_model: int, d_hidden: int):
+    x_grouped, w, m_sizes = _make_grouped_gemm_inputs(n_experts, d_model, d_hidden)
+    torch_w = _grouped_gemm_weight_for_torch(w, n_experts, d_hidden)
+    offs = _grouped_gemm_offs(m_sizes)
+    active = int(m_sizes.sum().item())
+
+    ref = F.grouped_mm(x_grouped, torch_w, offs=offs)
+    actual = grouped_gemm(x_grouped, w, m_sizes)
+    diff = (actual[:active].float() - ref[:active].float()).abs()
+    max_abs_diff = float(diff.max().item())
+    mean_abs_diff = float(diff.mean().item())
+
+    print(
+        f"\ngrouped_gemm vs torch grouped_mm diffs: "
+        f"max_abs_diff={max_abs_diff:.6f} "
+        f"mean_abs_diff={mean_abs_diff:.6f}"
+    )
+
+    assert torch.isfinite(actual[:active]).all()
+    assert actual[:active].shape == ref[:active].shape
+    assert max_abs_diff < 8.0
+
+
+@CUDA_ONLY
+@LOCAL_KERNELS_ONLY
+@pytest.mark.parametrize(("n_tokens", "n_experts", "top_k"), [(4096, 32, 4), (16384, 128, 4)])
+def test_index_shuffling_timing(n_tokens: int, n_experts: int, top_k: int, record_property, request):
+    _require_benchmark_flag(request)
+    scores = _random_scores(n_tokens, n_experts, "cuda")
+    custom_ms = _time_cuda_ms(lambda: index_shuffling(scores, top_k=top_k))
+    result = {"custom_ms": round(custom_ms, 4)}
+
+    if has_fbgemm_moe_kernels():
+        fbgemm_ms = _time_cuda_ms(lambda: fbgemm_index_shuffling(scores, top_k=top_k))
+        result["fbgemm_ms"] = round(fbgemm_ms, 4)
+        result["slowdown_vs_fbgemm"] = round(custom_ms / fbgemm_ms, 4)
+
+    record_property("index_shuffling_timing", result)
+    print(f"\nindex_shuffling timing: {result}")
+
+
+@CUDA_ONLY
+@LOCAL_KERNELS_ONLY
+@pytest.mark.parametrize(("n_out", "n_in", "width"), [(4096, 16384, 512), (8192, 32768, 1024)])
+def test_scatter_add_dense_tokens_timing(n_out: int, n_in: int, width: int, record_property, request):
+    _require_benchmark_flag(request)
+    in_tokens = torch.randn(n_in, width, device="cuda", dtype=torch.bfloat16).contiguous()
+    token_indices = torch.randint(0, n_out, (n_in,), device="cuda", dtype=torch.int64).contiguous()
+
+    def run_custom():
+        out = torch.zeros(n_out, width, device="cuda", dtype=in_tokens.dtype)
+        scatter_add_dense_tokens(out, in_tokens, token_indices)
+
+    custom_ms = _time_cuda_ms(run_custom)
+    result = {"custom_ms": round(custom_ms, 4)}
+
+    if has_fbgemm_moe_kernels():
+        def run_fbgemm():
+            out = torch.zeros(n_out, width, device="cuda", dtype=in_tokens.dtype)
+            fbgemm_scatter_add_dense_tokens(out, in_tokens, token_indices)
+
+        fbgemm_ms = _time_cuda_ms(run_fbgemm)
+        result["fbgemm_ms"] = round(fbgemm_ms, 4)
+        result["slowdown_vs_fbgemm"] = round(custom_ms / fbgemm_ms, 4)
+
+    record_property("scatter_add_dense_tokens_timing", result)
+    print(f"\nscatter_add_dense_tokens timing: {result}")
+
+
+@CUDA_ONLY
+@LOCAL_KERNELS_ONLY
+@pytest.mark.skipif(_load_fbgemm_grouped_gemm() is None, reason="fbgemm grouped_gemm is not available")
+@pytest.mark.parametrize(("n_experts", "d_model", "d_hidden"), [(8, 2048, 1024), (16, 2048, 1024)])
+def test_grouped_gemm_timing(n_experts: int, d_model: int, d_hidden: int, record_property, request):
+    _require_benchmark_flag(request)
+    x_grouped, w, m_sizes = _make_grouped_gemm_inputs(n_experts, d_model, d_hidden)
+
+    custom_ms = _time_cuda_ms(lambda: grouped_gemm(x_grouped, w, m_sizes))
+    fbgemm_ms = _time_cuda_ms(lambda: fbgemm_grouped_gemm(x_grouped, w, m_sizes))
+    result = {
+        "custom_ms": round(custom_ms, 4),
+        "fbgemm_ms": round(fbgemm_ms, 4),
+        "slowdown_vs_fbgemm": round(custom_ms / fbgemm_ms, 4),
+    }
+
+    record_property("grouped_gemm_timing", result)
+    print(f"\ngrouped_gemm timing: {result}")
+
+
+@CUDA_ONLY
+@LOCAL_KERNELS_ONLY
+@pytest.mark.parametrize(("n_experts", "d_model", "d_hidden"), [(8, 2048, 1024), (16, 2048, 1024)])
+def test_grouped_gemm_eager_vs_torch_grouped_mm_timing(
+    n_experts: int,
+    d_model: int,
+    d_hidden: int,
+    record_property,
+    request,
+):
+    _require_benchmark_flag(request)
+    x_grouped, w, m_sizes = _make_grouped_gemm_inputs(n_experts, d_model, d_hidden)
+    torch_w = _grouped_gemm_weight_for_torch(w, n_experts, d_hidden)
+    offs = _grouped_gemm_offs(m_sizes)
+
+    custom_ms = _time_cuda_ms(lambda: grouped_gemm(x_grouped, w, m_sizes))
+    torch_ms = _time_cuda_ms(lambda: F.grouped_mm(x_grouped, torch_w, offs=offs))
+    result = {
+        "custom_ms": round(custom_ms, 4),
+        "torch_grouped_mm_ms": round(torch_ms, 4),
+        "slowdown_vs_torch_grouped_mm": round(custom_ms / torch_ms, 4),
+    }
+
+    record_property("grouped_gemm_eager_vs_torch_grouped_mm_timing", result)
+    print(f"\ngrouped_gemm eager vs torch grouped_mm timing: {result}")
+
+
+@CUDA_ONLY
+@LOCAL_KERNELS_ONLY
+@pytest.mark.skipif(not has_fbgemm_moe_kernels(), reason="fbgemm MoE kernels are not available")
+@pytest.mark.parametrize(("n_experts", "top_k", "d_model", "d_hidden"), [(16, 8, 2048, 1024)])
+def test_moe_eager_timing_vs_fbgemm(
+    n_experts: int,
+    top_k: int,
+    d_model: int,
+    d_hidden: int,
+    record_property,
+    request,
+):
+    _require_benchmark_flag(request)
+    x, logits, expert_in_proj, expert_out_proj = _make_moe_inputs(
+        n_experts=n_experts,
+        top_k=top_k,
+        d_model=d_model,
+        d_hidden=d_hidden,
+    )
+
+    custom_out = _run_custom_moe_eager(x, logits, expert_in_proj, expert_out_proj, top_k)
+    moe_fbgemm, moe_fallback = _make_moe_modules(
+        n_experts=n_experts,
+        top_k=top_k,
+        d_model=d_model,
+        d_hidden=d_hidden,
+    )
+    with torch.no_grad():
+        moe_fbgemm.expert_in_proj.copy_(expert_in_proj)
+        moe_fbgemm.expert_out_proj.copy_(expert_out_proj)
+        moe_fallback.expert_in_proj.copy_(expert_in_proj)
+        moe_fallback.expert_out_proj.copy_(expert_out_proj)
+    fbgemm_out = _run_moe_module_eager(moe_fbgemm, x.unsqueeze(0), gate=logits.unsqueeze(0)).squeeze(0)
+    diff = (custom_out.float() - fbgemm_out.float()).abs()
+
+    custom_ms = _time_cuda_ms(lambda: _run_custom_moe_eager(x, logits, expert_in_proj, expert_out_proj, top_k))
+    fbgemm_ms = _time_cuda_ms(lambda: _run_moe_module_eager(moe_fbgemm, x.unsqueeze(0), gate=logits.unsqueeze(0)))
+    result = {
+        "custom_ms": round(custom_ms, 4),
+        "fbgemm_ms": round(fbgemm_ms, 4),
+        "slowdown_vs_fbgemm": round(custom_ms / fbgemm_ms, 4),
+        "max_abs_diff": round(float(diff.max().item()), 6),
+        "mean_abs_diff": round(float(diff.mean().item()), 6),
+    }
+
+    record_property("moe_eager_timing_vs_fbgemm", result)
+    print(f"\nmoe eager timing vs fbgemm: {result}")
+
+
+@CUDA_ONLY
+@pytest.mark.skipif(not has_fbgemm_moe_kernels(), reason="fbgemm MoE kernels are not available")
+@pytest.mark.parametrize(("n_experts", "top_k", "d_model", "d_hidden"), [(16, 8, 2048, 1024)])
+def test_fbgemm_moe_module_eager_matches_handwritten_eager(
+    n_experts: int,
+    top_k: int,
+    d_model: int,
+    d_hidden: int,
+):
+    moe_fbgemm, _ = _make_moe_modules(
+        n_experts=n_experts,
+        top_k=top_k,
+        d_model=d_model,
+        d_hidden=d_hidden,
+    )
+    x, logits, expert_in_proj, expert_out_proj = _make_moe_inputs(
+        n_experts=n_experts,
+        top_k=top_k,
+        d_model=d_model,
+        d_hidden=d_hidden,
+    )
+    with torch.no_grad():
+        moe_fbgemm.expert_in_proj.copy_(expert_in_proj)
+        moe_fbgemm.expert_out_proj.copy_(expert_out_proj)
+
+    module_out = _run_moe_module_eager(moe_fbgemm, x.unsqueeze(0), gate=logits.unsqueeze(0)).squeeze(0)
+    module_out_repeat = _run_moe_module_eager(moe_fbgemm, x.unsqueeze(0), gate=logits.unsqueeze(0)).squeeze(0)
+    loaded_out = _run_loaded_moe_eager(x, logits, expert_in_proj, expert_out_proj, top_k)
+    loaded_out_repeat = _run_loaded_moe_eager(x, logits, expert_in_proj, expert_out_proj, top_k)
+    fbgemm_out = _run_fbgemm_moe_eager(x, logits, expert_in_proj, expert_out_proj, top_k)
+    module_self_diff = (module_out.float() - module_out_repeat.float()).abs()
+    loaded_self_diff = (loaded_out.float() - loaded_out_repeat.float()).abs()
+    loaded_diff = (module_out.float() - loaded_out.float()).abs()
+    fbgemm_diff = (module_out.float() - fbgemm_out.float()).abs()
+
+    print(
+        f"\nworld_model symbols: "
+        f"fbgemm_index_shuffling={world_model_module.fbgemm_index_shuffling!r} "
+        f"custom_index_shuffling={world_model_module.custom_index_shuffling!r} "
+        f"scatter_add={torch.ops.fbgemm.scatter_add_dense_tokens!r}"
+    )
+    print(
+        f"module self diffs: "
+        f"max_abs_diff={float(module_self_diff.max().item()):.6f} "
+        f"mean_abs_diff={float(module_self_diff.mean().item()):.6f}"
+    )
+    print(
+        f"loaded handwritten self diffs: "
+        f"max_abs_diff={float(loaded_self_diff.max().item()):.6f} "
+        f"mean_abs_diff={float(loaded_self_diff.mean().item()):.6f}"
+    )
+    print(
+        f"module vs loaded handwritten diffs: "
+        f"max_abs_diff={float(loaded_diff.max().item()):.6f} "
+        f"mean_abs_diff={float(loaded_diff.mean().item()):.6f}"
+    )
+    print(
+        f"module vs old fbgemm handwritten diffs: "
+        f"max_abs_diff={float(fbgemm_diff.max().item()):.6f} "
+        f"mean_abs_diff={float(fbgemm_diff.mean().item()):.6f}"
+    )
+
+    max_self_max = max(
+        float(module_self_diff.max().item()),
+        float(loaded_self_diff.max().item()),
+    )
+    max_self_mean = max(
+        float(module_self_diff.mean().item()),
+        float(loaded_self_diff.mean().item()),
+    )
+    assert float(loaded_diff.max().item()) <= max_self_max + 8.0
+    assert float(loaded_diff.mean().item()) <= max_self_mean + 0.05
+
+
+@CUDA_ONLY
+@pytest.mark.skipif(not has_fbgemm_moe_kernels(), reason="fbgemm MoE kernels are not available")
+@pytest.mark.parametrize(("n_experts", "top_k", "d_model", "d_hidden"), [(16, 8, 2048, 1024)])
+def test_fbgemm_moe_repeated_run_heads(
+    n_experts: int,
+    top_k: int,
+    d_model: int,
+    d_hidden: int,
+):
+    torch.manual_seed(0)
+    torch.cuda.manual_seed_all(0)
+    moe_fbgemm, _ = _make_moe_modules(
+        n_experts=n_experts,
+        top_k=top_k,
+        d_model=d_model,
+        d_hidden=d_hidden,
+    )
+    x, logits, expert_in_proj, expert_out_proj = _make_moe_inputs(
+        n_experts=n_experts,
+        top_k=top_k,
+        d_model=d_model,
+        d_hidden=d_hidden,
+    )
+    with torch.no_grad():
+        moe_fbgemm.expert_in_proj.copy_(expert_in_proj)
+        moe_fbgemm.expert_out_proj.copy_(expert_out_proj)
+
+    module_1 = _run_moe_module_eager(moe_fbgemm, x.unsqueeze(0), gate=logits.unsqueeze(0)).squeeze(0)
+    module_2 = _run_moe_module_eager(moe_fbgemm, x.unsqueeze(0), gate=logits.unsqueeze(0)).squeeze(0)
+    loaded_1 = _run_loaded_moe_eager(x, logits, expert_in_proj, expert_out_proj, top_k)
+    loaded_2 = _run_loaded_moe_eager(x, logits, expert_in_proj, expert_out_proj, top_k)
+    fbgemm_1 = _run_fbgemm_moe_eager(x, logits, expert_in_proj, expert_out_proj, top_k)
+    fbgemm_2 = _run_fbgemm_moe_eager(x, logits, expert_in_proj, expert_out_proj, top_k)
+
+    for name, lhs, rhs in [
+        ("module", module_1, module_2),
+        ("loaded", loaded_1, loaded_2),
+        ("old_fbgemm", fbgemm_1, fbgemm_2),
+    ]:
+        diff = (lhs.float() - rhs.float()).abs()
+        print(f"\n[{name}] run1 head: {lhs[0, :16].float().cpu().tolist()}")
+        print(f"[{name}] run2 head: {rhs[0, :16].float().cpu().tolist()}")
+        print(f"[{name}] abs diff head: {diff[0, :16].cpu().tolist()}")
+        print(
+            f"[{name}] max_abs_diff={float(diff.max().item()):.6f} "
+            f"mean_abs_diff={float(diff.mean().item()):.6f}"
+        )
+
+
+@CUDA_ONLY
+# @pytest.mark.skipif(not has_fbgemm_moe_kernels(), reason="fbgemm MoE kernels are not available")
+@pytest.mark.parametrize(("n_experts", "top_k", "d_model", "d_hidden"), [(16, 8, 2048, 1024)])
+def test_fbgemm_moe_eager_vs_compiled_timing(
+    n_experts: int,
+    top_k: int,
+    d_model: int,
+    d_hidden: int,
+    record_property,
+    request,
+):
+    _require_benchmark_flag(request)
+    moe_fbgemm, _ = _make_moe_modules(
+        n_experts=n_experts,
+        top_k=top_k,
+        d_model=d_model,
+        d_hidden=d_hidden,
+    )
+    x = torch.randn((1, 512, d_model), device="cuda", dtype=torch.bfloat16)
+
+    eager_out = _run_moe_module_eager(moe_fbgemm, x)
+    compiled_out = _run_moe_module_compiled_inference(moe_fbgemm, x)
+    diff = (compiled_out.float() - eager_out.float()).abs()
+
+    eager_ms = _time_cuda_ms(lambda: _run_moe_module_eager(moe_fbgemm, x))
+    compiled_ms = _time_cuda_ms(lambda: _run_moe_module_compiled_inference(moe_fbgemm, x))
+    result = {
+        "eager_ms": round(eager_ms, 4),
+        "compiled_ms": round(compiled_ms, 4),
+        "speedup_vs_eager": round(eager_ms / compiled_ms, 4),
+        "max_abs_diff": round(float(diff.max().item()), 6),
+        "mean_abs_diff": round(float(diff.mean().item()), 6),
+    }
+
+    record_property("fbgemm_moe_eager_vs_compiled_timing", result)
+    print(f"\nfbgemm moe eager vs compiled timing: {result}")
+
+
+@CUDA_ONLY
+@pytest.mark.skipif(not has_fbgemm_moe_kernels(), reason="fbgemm MoE kernels are not available")
+@pytest.mark.parametrize(("n_experts", "top_k", "d_model", "d_hidden"), [(16, 8, 2048, 1024)])
+def test_moe_module_forward_matches_fbgemm_eager_and_compiled(
+    n_experts: int,
+    top_k: int,
+    d_model: int,
+    d_hidden: int,
+):
+    moe_fbgemm, moe_fallback = _make_moe_modules(
+        n_experts=n_experts,
+        top_k=top_k,
+        d_model=d_model,
+        d_hidden=d_hidden,
+    )
+    x = torch.randn((1, 512, d_model), device="cuda", dtype=torch.bfloat16)
+
+    eager_fbgemm = _run_moe_module_eager(moe_fbgemm, x)
+    eager_fallback = _run_moe_module_eager(moe_fallback, x)
+    compiled_fbgemm = _run_moe_module_compiled_inference(moe_fbgemm, x)
+
+    fallback_diff = (eager_fallback.float() - eager_fbgemm.float()).abs()
+    compiled_diff = (compiled_fbgemm.float() - eager_fbgemm.float()).abs()
+
+    print(
+        f"\nmoe module diffs: "
+        f"fallback_max_abs_diff={float(fallback_diff.max().item()):.6f} "
+        f"fallback_mean_abs_diff={float(fallback_diff.mean().item()):.6f} "
+        f"compiled_max_abs_diff={float(compiled_diff.max().item()):.6f} "
+        f"compiled_mean_abs_diff={float(compiled_diff.mean().item()):.6f}"
+    )
+
+    assert torch.isfinite(eager_fallback).all()
+    assert torch.isfinite(compiled_fbgemm).all()
+
+
+@CUDA_ONLY
+@pytest.mark.skipif(not has_fbgemm_moe_kernels(), reason="fbgemm MoE kernels are not available")
+@pytest.mark.parametrize(("n_experts", "top_k", "d_model", "d_hidden"), [(16, 8, 2048, 1024)])
+def test_moe_module_timing_vs_fbgemm_eager_and_compiled(
+    n_experts: int,
+    top_k: int,
+    d_model: int,
+    d_hidden: int,
+    record_property,
+    request,
+):
+    _require_benchmark_flag(request)
+    moe_fbgemm, moe_fallback = _make_moe_modules(
+        n_experts=n_experts,
+        top_k=top_k,
+        d_model=d_model,
+        d_hidden=d_hidden,
+    )
+    x = torch.randn((1, 512, d_model), device="cuda", dtype=torch.bfloat16)
+
+    eager_fbgemm = _run_moe_module_eager(moe_fbgemm, x)
+    eager_fallback = _run_moe_module_eager(moe_fallback, x)
+    compiled_fbgemm = _run_moe_module_compiled_inference(moe_fbgemm, x)
+    fallback_diff = (eager_fallback.float() - eager_fbgemm.float()).abs()
+    compiled_diff = (compiled_fbgemm.float() - eager_fbgemm.float()).abs()
+
+    fallback_ms = _time_cuda_ms(lambda: _run_moe_module_eager(moe_fallback, x))
+    eager_fbgemm_ms = _time_cuda_ms(lambda: _run_moe_module_eager(moe_fbgemm, x))
+    compiled_fbgemm_ms = _time_cuda_ms(lambda: _run_moe_module_compiled_inference(moe_fbgemm, x))
+    result = {
+        "fallback_ms": round(fallback_ms, 4),
+        "eager_fbgemm_ms": round(eager_fbgemm_ms, 4),
+        "compiled_fbgemm_ms": round(compiled_fbgemm_ms, 4),
+        "fallback_slowdown_vs_eager_fbgemm": round(fallback_ms / eager_fbgemm_ms, 4),
+        "fallback_slowdown_vs_compiled_fbgemm": round(fallback_ms / compiled_fbgemm_ms, 4),
+        "compiled_speedup_vs_eager_fbgemm": round(eager_fbgemm_ms / compiled_fbgemm_ms, 4),
+        "fallback_max_abs_diff": round(float(fallback_diff.max().item()), 6),
+        "fallback_mean_abs_diff": round(float(fallback_diff.mean().item()), 6),
+        "compiled_max_abs_diff": round(float(compiled_diff.max().item()), 6),
+        "compiled_mean_abs_diff": round(float(compiled_diff.mean().item()), 6),
+    }
+
+    record_property("moe_module_timing_vs_fbgemm_eager_and_compiled", result)
+    print(f"\nmoe module timing vs fbgemm eager and compiled: {result}")
+
+
+@CUDA_ONLY
+@pytest.mark.skipif(not has_fbgemm_moe_kernels(), reason="fbgemm MoE kernels are not available")
+@pytest.mark.parametrize(("n_experts", "top_k", "d_model", "d_hidden"), [(16, 8, 2048, 1024)])
+def test_moe_module_compiled_timing_vs_fbgemm_compiled(
+    n_experts: int,
+    top_k: int,
+    d_model: int,
+    d_hidden: int,
+    record_property,
+    request,
+):
+    _require_benchmark_flag(request)
+    moe_fbgemm, moe_fallback = _make_moe_modules(
+        n_experts=n_experts,
+        top_k=top_k,
+        d_model=d_model,
+        d_hidden=d_hidden,
+    )
+    x = torch.randn((1, 512, d_model), device="cuda", dtype=torch.bfloat16)
+
+    compiled_fbgemm = _run_moe_module_compiled_inference(moe_fbgemm, x)
+    compiled_fallback = _run_moe_fallback_module_compiled_inference(moe_fallback, x)
+    compiled_diff = (compiled_fallback.float() - compiled_fbgemm.float()).abs()
+
+    compiled_fallback_ms = _time_cuda_ms(lambda: _run_moe_fallback_module_compiled_inference(moe_fallback, x))
+    compiled_fbgemm_ms = _time_cuda_ms(lambda: _run_moe_module_compiled_inference(moe_fbgemm, x))
+    result = {
+        "compiled_fallback_ms": round(compiled_fallback_ms, 4),
+        "compiled_fbgemm_ms": round(compiled_fbgemm_ms, 4),
+        "fallback_slowdown_vs_compiled_fbgemm": round(compiled_fallback_ms / compiled_fbgemm_ms, 4),
+        "compiled_max_abs_diff": round(float(compiled_diff.max().item()), 6),
+        "compiled_mean_abs_diff": round(float(compiled_diff.mean().item()), 6),
+    }
+
+    record_property("moe_module_compiled_timing_vs_fbgemm_compiled", result)
+    print(f"\nmoe module compiled timing vs fbgemm compiled: {result}")
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..1561c6d
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,1680 @@
+version = 1
+revision = 3
+requires-python = ">=3.10"
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