submission 751825

- #!POPCORN leaderboard amd-moe-mxfp4
- #!POPCORN gpu MI355X
- """
- MXFP4 MoE Optimization V2 - Optimized for MI355X.
- Key optimizations:
- 1. Ensure proper padding and alignment
- 2. Minimize memory allocation
- 3. Optimize for DeepSeek-R1 architecture
- """
- import torch
- from typing import Dict
- from task import input_t, output_t
- 
- from aiter import ActivationType, QuantType
- from aiter.fused_moe import fused_moe
- 
- 
- def custom_kernel(data: input_t) -> output_t:
-     """
-     Optimized MXFP4 MoE implementation.
-     """
-     (
-         hidden_states,
-         gate_up_weight,
-         down_weight,
-         gate_up_weight_scale,
-         down_weight_scale,
-         gate_up_weight_shuffled,
-         down_weight_shuffled,
-         gate_up_weight_scale_shuffled,
-         down_weight_scale_shuffled,
-         topk_weights,
-         topk_ids,
-         config,
-     ) = data
- 
-     hidden_pad = config["d_hidden_pad"] - config["d_hidden"]
-     intermediate_pad = config["d_expert_pad"] - config["d_expert"]
- 
-     # Use the pre-shuffled weights for best performance
-     output = fused_moe(
-         hidden_states,
-         gate_up_weight_shuffled,
-         down_weight_shuffled,
-         topk_weights,
-         topk_ids,
-         expert_mask=None,
-         activation=ActivationType.Silu,
-         quant_type=QuantType.per_1x32,
-         doweight_stage1=False,
-         w1_scale=gate_up_weight_scale_shuffled,
-         w2_scale=down_weight_scale_shuffled,
-         a1_scale=None,
-         a2_scale=None,
-         hidden_pad=hidden_pad,
-         intermediate_pad=intermediate_pad,
-     )
- 
-     return output
   No newline at end of file
+ #!POPCORN leaderboard amd-moe-mxfp4
+ #!POPCORN gpu MI355X
+  
+ """
+ v168: Pre-allocate quantization output buffers (x_fp4, blockscale_e8m0_sorted)
+ for both stage1 and stage2 quant calls. Inline the fused_dynamic_mxfp4_quant_moe_sort
+ Triton kernel launch with cached output tensors to eliminate 4 torch.empty allocations
+ per forward pass on CK 2-stage shapes.
+ """
+ import os
+ import functools
+ import torch
+ import triton
+ from typing import Dict, Tuple, Optional
+ from task import input_t, output_t
+  
+ import aiter
+ from aiter import ActivationType, QuantType, dtypes
+ from aiter.fused_moe import (
+     get_2stage_cfgs, get_padded_M, get_inter_dim,
+     ck_moe_stage1, cktile_moe_stage1, cktile_moe_stage2,
+     _flydsl_stage2_wrapper,
+ )
+ import aiter.fused_moe as _moe_module
+ import aiter.ops.flydsl.moe_kernels as _flydsl_kernels
+ from aiter.ops.triton._triton_kernels.quant.fused_mxfp4_quant import (
+     _fused_dynamic_mxfp4_quant_moe_sort_kernel,
+ )
+ from aiter.utility import fp4_utils
+  
+ # Register FlyDSL tile_k=128 kernels
+ _flydsl_kernels._KERNEL_PARAMS["flydsl_moe2_afp4_wfp4_bf16_t32x128x128_atomic"] = {
+     "stage": 2, "a_dtype": "fp4", "b_dtype": "fp4", "out_dtype": "bf16",
+     "tile_m": 32, "tile_n": 128, "tile_k": 128, "mode": "atomic", "MPerBlock": 32,
+ }
+ _flydsl_kernels._KERNEL_PARAMS["flydsl_moe2_afp4_wfp4_bf16_t32x256x128_atomic"] = {
+     "stage": 2, "a_dtype": "fp4", "b_dtype": "fp4", "out_dtype": "bf16",
+     "tile_m": 32, "tile_n": 256, "tile_k": 128, "mode": "atomic", "MPerBlock": 32,
+ }
+ _flydsl_kernels._KERNEL_PARAMS["flydsl_moe2_afp4_wfp4_bf16_t16x256x128_atomic"] = {
+     "stage": 2, "a_dtype": "fp4", "b_dtype": "fp4", "out_dtype": "bf16",
+     "tile_m": 16, "tile_n": 256, "tile_k": 128, "mode": "atomic", "MPerBlock": 16,
+ }
+ _flydsl_kernels._KERNEL_PARAMS["flydsl_moe2_afp4_wfp4_bf16_t16x128x128_atomic"] = {
+     "stage": 2, "a_dtype": "fp4", "b_dtype": "fp4", "out_dtype": "bf16",
+     "tile_m": 16, "tile_n": 128, "tile_k": 128, "mode": "atomic", "MPerBlock": 16,
+ }
+  
+ # Shape configs
+ _SHAPE_PARAMS = {}
+  
+ def _gen_shape_id(token, inter_dim, expert):
+     return (
+         256, token, 7168, inter_dim, expert, 9,
+         "ActivationType.Silu", "torch.bfloat16",
+         "torch.float4_e2m1fn_x2", "torch.float4_e2m1fn_x2",
+         "QuantType.per_1x32", True, False,
+     )
+  
+ _S1_4WG_M128 = "moe_ck2stages_gemm1_256x128x128x128_1x4_MulABScaleShuffled_v3_Nswizzle0_Quant3_MulRoutedWeight0_silu_FP4X2_FP4X2_B16"
+ _S1_4WG_M32 = "moe_ck2stages_gemm1_256x32x128x128_1x4_MulABScaleShuffled_v3_Nswizzle0_Quant3_MulRoutedWeight0_silu_FP4X2_FP4X2_B16"
+ _S2_FLYDSL_M16_K128 = "flydsl_moe2_afp4_wfp4_bf16_t16x128x128_atomic"
+  
+ # E=33 shapes
+ _SHAPE_PARAMS[_gen_shape_id(16, 512, 33)] = {
+     "block_m": 32, "ksplit": 2, "kernelName1": "", "kernelName2": "",
+     "run_1stage": False,
+ }
+ _SHAPE_PARAMS[_gen_shape_id(128, 512, 33)] = {
+     "block_m": 64, "ksplit": 0,
+     "kernelName1": _S1_4WG_M128, "kernelName2": _S2_FLYDSL_M16_K128,
+     "run_1stage": False,
+ }
+ _SHAPE_PARAMS[_gen_shape_id(512, 512, 33)] = {
+     "block_m": 64, "ksplit": 0,
+     "kernelName1": _S1_4WG_M128, "kernelName2": _S2_FLYDSL_M16_K128,
+     "run_1stage": False,
+ }
+ _SHAPE_PARAMS[_gen_shape_id(512, 2048, 33)] = {
+     "block_m": 64, "ksplit": 0,
+     "kernelName1": _S1_4WG_M128, "kernelName2": _S2_FLYDSL_M16_K128,
+     "run_1stage": False,
+ }
+  
+ # E=257 shapes
+ _SHAPE_PARAMS[_gen_shape_id(16, 256, 257)] = {
+     "block_m": 16, "ksplit": 2, "kernelName1": "", "kernelName2": "",
+     "run_1stage": False,
+ }
+ _SHAPE_PARAMS[_gen_shape_id(128, 256, 257)] = {
+     "block_m": 16, "ksplit": 2, "kernelName1": "", "kernelName2": "",
+     "run_1stage": False,
+ }
+ _SHAPE_PARAMS[_gen_shape_id(512, 256, 257)] = {
+     "block_m": 32, "ksplit": 0,
+     "kernelName1": _S1_4WG_M32, "kernelName2": _S2_FLYDSL_M16_K128,
+     "run_1stage": False,
+     "use_non_temporal_load": True,
+ }
+  
+ # Pre-allocated buffer cache
+ _tensor_pool = {}
+  
+ def _acquire_sort_tensors(M, E, topk, model_dim, block_size_M, device):
+     """Pre-allocate moe_sorting output buffers."""
+     key = ("sort", M, E, topk, model_dim, block_size_M)
+     if key in _tensor_pool:
+         return _tensor_pool[key]
+  
+     max_num_tokens_padded = int(M * topk + E * block_size_M - topk)
+     max_num_m_blocks = int((max_num_tokens_padded + block_size_M - 1) // block_size_M)
+  
+     bufs = {
+         "sorted_ids": torch.empty(max_num_tokens_padded, dtype=dtypes.i32, device=device),
+         "sorted_weights": torch.empty(max_num_tokens_padded, dtype=dtypes.fp32, device=device),
+         "sorted_expert_ids": torch.empty(max_num_m_blocks, dtype=dtypes.i32, device=device),
+         "num_valid_ids": torch.empty(2, dtype=dtypes.i32, device=device),
+         "moe_buf": torch.empty((M, model_dim), dtype=torch.bfloat16, device=device),
+     }
+     _tensor_pool[key] = bufs
+     return bufs
+  
+ def _acquire_a2_tensor(M, topk, inter_dim, device):
+     """Pre-allocate a2 intermediate buffer."""
+     key = ("a2", M, topk, inter_dim)
+     if key in _tensor_pool:
+         return _tensor_pool[key]
+     buf = torch.empty((M, topk, inter_dim), dtype=torch.bfloat16, device=device)
+     _tensor_pool[key] = buf
+     return buf
+  
+ def _acquire_quant_tensors(M, N, sorted_ids_len, topk, device):
+     """Pre-allocate quantization output buffers for fused_dynamic_mxfp4_quant_moe_sort."""
+     FP4_BLK_SZ = 32
+     TILE_M, TILE_N = 32, 8
+     TILE_M_u32, TILE_N_u32 = 16, 4
+  
+     key = ("quant", M, N, sorted_ids_len, topk)
+     if key in _tensor_pool:
+         return _tensor_pool[key]
+  
+     x_fp4 = torch.empty((M, N // 2), dtype=torch.uint8, device=device)
+     scaleN = triton.cdiv(N, FP4_BLK_SZ)
+     M_o = sorted_ids_len
+     N_o = scaleN
+  
+     blockscale_e8m0_sorted = torch.empty(
+         (
+             triton.cdiv(M_o, TILE_M),
+             triton.cdiv(N_o, TILE_N),
+             TILE_N_u32,
+             TILE_M_u32,
+             4,
+         ),
+         dtype=torch.uint8,
+         device=device,
+     )
+  
+     bufs = {"x_fp4": x_fp4, "blockscale": blockscale_e8m0_sorted}
+     _tensor_pool[key] = bufs
+     return bufs
+  
+ def _quant_with_cached_out(x, sorted_ids, num_valid_ids, token_num, topk, block_size, device):
+     """Inline fused_dynamic_mxfp4_quant_moe_sort with pre-allocated output buffers."""
+     M, N = x.shape
+     FP4_BLK_SZ = 32
+     TILE_Mx = 128
+     TILE_M, TILE_N = 32, 8
+  
+     scaleN = triton.cdiv(N, FP4_BLK_SZ)
+     M_i, N_i = M, scaleN
+     M_o = sorted_ids.shape[0]
+  
+     # Get pre-allocated buffers
+     qbufs = _acquire_quant_tensors(M, N, M_o, topk, device)
+     x_fp4 = qbufs["x_fp4"]
+     blockscale_e8m0_sorted = qbufs["blockscale"]
+  
+     num_pid = triton.cdiv(M, TILE_Mx) * scaleN + triton.cdiv(
+         M_o, TILE_M
+     ) * triton.cdiv(N_i, TILE_N)
+  
+     _fused_dynamic_mxfp4_quant_moe_sort_kernel[(num_pid,)](
+         x,
+         x_fp4,
+         sorted_ids,
+         num_valid_ids,
+         blockscale_e8m0_sorted,
+         M,
+         N,
+         scaleN,
+         *x.stride(),
+         *x_fp4.stride(),
+         *blockscale_e8m0_sorted.stride(),
+         token_num=token_num,
+         M_i=M_i,
+         N_i=N_i,
+         MXFP4_QUANT_BLOCK_SIZE=FP4_BLK_SZ,
+         BLOCK_SIZE_Mx=TILE_Mx,
+         BLOCK_SIZE_M=TILE_M // 2,
+         BLOCK_SIZE_N=TILE_N // 2,
+         TOPK=topk,
+     )
+  
+     return (
+         x_fp4.view(dtypes.fp4x2),
+         blockscale_e8m0_sorted.view(dtypes.fp8_e8m0).view(-1, scaleN),
+     )
+  
+  
+ _patched = False
+  
+ def _apply_shape_overrides():
+     global _patched
+     if _patched:
+         return
+     _patched = True
+  
+     if _moe_module.cfg_2stages is None:
+         import pandas as pd
+         from aiter.jit.core import AITER_CONFIGS
+         tune_file = AITER_CONFIGS.AITER_CONFIG_FMOE_FILE
+         if os.path.exists(tune_file):
+             _INDEX_COLS = [
+                 "cu_num", "token", "model_dim", "inter_dim", "expert", "topk",
+                 "act_type", "dtype", "q_dtype_a", "q_dtype_w", "q_type",
+                 "use_g1u1", "doweight_stage1",
+             ]
+             df = pd.read_csv(tune_file)
+             if "_tag" in df.columns:
+                 df = df[df["_tag"].fillna("") == ""]
+             _moe_module.cfg_2stages = df.set_index(_INDEX_COLS).to_dict("index")
+         else:
+             _moe_module.cfg_2stages = {}
+  
+     _moe_module.cfg_2stages.update(_SHAPE_PARAMS)
+  
+     # Monkeypatch get_2stage_cfgs to support use_non_temporal_load from config
+     _orig_get_2stage_cfgs = _moe_module.get_2stage_cfgs
+  
+     @functools.lru_cache(maxsize=2048)
+     def _custom_get_2stage_cfgs(
+         token, model_dim, inter_dim, expert, topk,
+         dtype, q_dtype_a, q_dtype_w, q_type, use_g1u1,
+         activation, doweight_stage1, hidden_pad, intermediate_pad, is_shuffled=True,
+     ):
+         metadata = _orig_get_2stage_cfgs(
+             token, model_dim, inter_dim, expert, topk,
+             dtype, q_dtype_a, q_dtype_w, q_type, use_g1u1,
+             activation, doweight_stage1, hidden_pad, intermediate_pad, is_shuffled,
+         )
+         from aiter.jit.utils.chip_info import get_cu_num
+         cu_num = get_cu_num()
+         keys = (
+             cu_num, token, model_dim, inter_dim, expert, topk,
+             str(activation), str(dtype), str(q_dtype_a), str(q_dtype_w),
+             str(q_type), use_g1u1, doweight_stage1,
+         )
+         cfg = _moe_module.cfg_2stages.get(keys)
+         if cfg and cfg.get("use_non_temporal_load") is not None:
+             nt = cfg["use_non_temporal_load"]
+             old_s1 = metadata.stage1
+             if hasattr(old_s1, 'func') and old_s1.func is not None:
+                 if 'use_non_temporal_load' in (old_s1.keywords or {}):
+                     new_kw = dict(old_s1.keywords)
+                     new_kw['use_non_temporal_load'] = nt
+                     metadata = _moe_module.MOEMetadata(
+                         functools.partial(old_s1.func, **{k: v for k, v in new_kw.items()}),
+                         metadata.stage2,
+                         metadata.block_m,
+                         metadata.ksplit,
+                         metadata.run_1stage,
+                         metadata.has_bias,
+                         nt,
+                     )
+                     old_s2 = metadata.stage2
+                     if old_s2 and hasattr(old_s2, 'keywords') and 'use_non_temporal_load' in (old_s2.keywords or {}):
+                         new_kw2 = dict(old_s2.keywords)
+                         new_kw2['use_non_temporal_load'] = nt
+                         metadata = _moe_module.MOEMetadata(
+                             metadata.stage1,
+                             functools.partial(old_s2.func, **{k: v for k, v in new_kw2.items()}),
+                             metadata.block_m,
+                             metadata.ksplit,
+                             metadata.run_1stage,
+                             metadata.has_bias,
+                             nt,
+                         )
+         return metadata
+  
+     _moe_module.get_2stage_cfgs = _custom_get_2stage_cfgs
+  
+  
+ def custom_kernel(data: input_t) -> output_t:
+     (
+         hidden_states, gate_up_weight, down_weight,
+         gate_up_weight_scale, down_weight_scale,
+         gate_up_weight_shuffled, down_weight_shuffled,
+         gate_up_weight_scale_shuffled, down_weight_scale_shuffled,
+         topk_weights, topk_ids, config,
+     ) = data
+  
+     _apply_shape_overrides()
+  
+     hidden_pad = config["d_hidden_pad"] - config["d_hidden"]
+     intermediate_pad = config["d_expert_pad"] - config["d_expert"]
+  
+     M = hidden_states.shape[0]
+     topk = topk_ids.shape[1]
+     device = topk_ids.device
+     w1 = gate_up_weight_shuffled
+     w2 = down_weight_shuffled
+     E, model_dim, inter_dim = get_inter_dim(w1.shape, w2.shape)
+  
+     padded_M = get_padded_M(M)
+     metadata = get_2stage_cfgs(
+         padded_M, model_dim, inter_dim, E, topk,
+         torch.bfloat16, dtypes.fp4x2, dtypes.fp4x2,
+         QuantType.per_1x32, True, ActivationType.Silu,
+         False, hidden_pad, intermediate_pad, True,
+     )
+  
+     block_size_M = int(metadata.block_m)
+  
+     # === Pre-allocated moe_sorting ===
+     bufs = _acquire_sort_tensors(M, E, topk, model_dim, block_size_M, device)
+     sorted_ids = bufs["sorted_ids"]
+     sorted_weights = bufs["sorted_weights"]
+     sorted_expert_ids = bufs["sorted_expert_ids"]
+     num_valid_ids = bufs["num_valid_ids"]
+     moe_out = bufs["moe_buf"]
+  
+     aiter.moe_sorting_fwd(
+         topk_ids, topk_weights,
+         sorted_ids, sorted_weights, sorted_expert_ids, num_valid_ids, moe_out,
+         E, int(block_size_M), None, None, 0,
+     )
+  
+     # === Inline 2-stage pipeline ===
+     token_num = M
+  
+     if metadata.ksplit > 1:
+         # cktile_moe path: bf16 activations, no fp4 quant
+         a1 = hidden_states.to(torch.bfloat16)
+         a1_scale = None
+         w1_scale_view = gate_up_weight_scale_shuffled.view(dtypes.fp8_e8m0)
+         w2_scale_view = down_weight_scale_shuffled.view(dtypes.fp8_e8m0)
+  
+         a2 = metadata.stage1(
+             a1, w1, w2,
+             sorted_ids, sorted_expert_ids, num_valid_ids,
+             _acquire_a2_tensor(M, topk, inter_dim, device),  # pre-allocated
+             topk,
+             block_m=block_size_M,
+             a1_scale=a1_scale,
+             w1_scale=w1_scale_view,
+             sorted_weights=None,
+         )
+  
+         # cktile_moe stage2: a2 is bf16, no inter-stage requant
+         a2_scale = None
+         metadata.stage2(
+             a2, w1, w2,
+             sorted_ids, sorted_expert_ids, num_valid_ids,
+             moe_out, topk,
+             w2_scale=w2_scale_view,
+             a2_scale=a2_scale,
+             block_m=block_size_M,
+             sorted_weights=sorted_weights,
+         )
+     else:
+         # CK 2-stage path: fp4 activation quant with pre-allocated buffers
+         w1_scale_view = gate_up_weight_scale_shuffled.view(dtypes.fp8_e8m0)
+         w2_scale_view = down_weight_scale_shuffled.view(dtypes.fp8_e8m0)
+  
+         # Stage 1: quant activations + gate_up GEMM + SwiGLU
+         a1, a1_scale = _quant_with_cached_out(
+             hidden_states, sorted_ids, num_valid_ids,
+             token_num, 1, block_size_M, device,
+         )
+  
+         a2 = _acquire_a2_tensor(M, topk, inter_dim, device)
+         a2 = metadata.stage1(
+             a1, w1, w2,
+             sorted_ids, sorted_expert_ids, num_valid_ids,
+             a2, topk,
+             block_m=block_size_M,
+             a1_scale=a1_scale,
+             w1_scale=w1_scale_view,
+             sorted_weights=None,
+         )
+  
+         # Inter-stage requant: bf16 -> fp4 with pre-allocated buffers
+         a2_flat = a2.view(-1, inter_dim)
+         a2_quant, a2_scale = _quant_with_cached_out(
+             a2_flat, sorted_ids, num_valid_ids,
+             token_num, topk, block_size_M, device,
+         )
+         a2_quant = a2_quant.view(token_num, topk, -1)
+  
+         # Stage 2: down GEMM + weighted reduction
+         metadata.stage2(
+             a2_quant, w1, w2,
+             sorted_ids, sorted_expert_ids, num_valid_ids,
+             moe_out, topk,
+             w2_scale=w2_scale_view,
+             a2_scale=a2_scale,
+             block_m=block_size_M,
+             sorted_weights=sorted_weights,
+         )
+  
+     return moe_out
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