import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Dict, Tuple, Optional, TypeVar

# Type definitions
input_t = TypeVar("input_t", bound=Tuple[torch.Tensor, Dict[str, torch.Tensor], Dict])
output_t_workaround = torch.Tensor # Workaround for evaluator expecting a single tensor

class Expert(nn.Module):
    def __init__(self, config: Dict, d_expert: Optional[int] = None):
        super().__init__()
        self.config = config
        self.act_fn = nn.SiLU()
        self.d_hidden: int = config["d_hidden"]
        self.d_expert: int = config["d_expert"] if d_expert is None else d_expert

        self.W_gate = nn.Linear(self.d_hidden, self.d_expert, bias=False)
        self.W_up = nn.Linear(self.d_hidden, self.d_expert, bias=False)
        self.W_down = nn.Linear(self.d_expert, self.d_hidden, bias=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        gate_out = self.W_gate(x)
        up_out = self.W_up(x)
        activated_gate = self.act_fn(gate_out)
        fused_expert_op = activated_gate * up_out
        out = self.W_down(fused_expert_op)
        return out

class MoEGate(nn.Module):
    def __init__(self, config: Dict):
        super().__init__()
        self.top_k: int = config["n_experts_per_token"]
        self.num_experts: int = config["n_routed_experts"]
        self.d_hidden: int = config["d_hidden"]
        self.W_g = nn.Linear(self.d_hidden, self.num_experts, bias=False)

    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        logits = self.W_g(x)
        scores = logits.softmax(dim=-1)
        topk_scores, topk_indices = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
        return topk_indices, topk_scores

class MoE(nn.Module):
    def __init__(self, config: Dict):
        super().__init__()
        self.config = config
        self.num_experts = config["n_routed_experts"]
        self.top_k = config["n_experts_per_token"]
        self.experts = nn.ModuleList([
            Expert(config)
            for _ in range(self.num_experts)
        ])
        self.gating_network = MoEGate(config)
        shared_expert_dim = config["d_expert"] * config["n_shared_experts"]
        self.shared_expert = Expert(config=config, d_expert=shared_expert_dim)

    def _moe_dispatch_pytorch_fallback(self,
                                 x_flat_for_dispatch: torch.Tensor,
                                 flat_expert_indices: torch.Tensor,
                                 flat_expert_scores: torch.Tensor
                                ) -> torch.Tensor:
        expert_output_accumulator = torch.zeros_like(x_flat_for_dispatch)
        
        sorted_indices = flat_expert_indices.argsort()
        original_token_indices_sorted = sorted_indices // self.top_k

        tokens_per_expert_counts = flat_expert_indices.bincount(minlength=self.num_experts)
        expert_dispatch_boundaries = tokens_per_expert_counts.cumsum(dim=0)

        for expert_id in range(self.num_experts):
            start_idx_tensor = expert_dispatch_boundaries[expert_id-1] if expert_id > 0 else torch.tensor(0)
            end_idx_tensor = expert_dispatch_boundaries[expert_id]
            
            start_idx_val = start_idx_tensor.item()
            end_idx_val = end_idx_tensor.item()

            if start_idx_val == end_idx_val:
                continue

            indices_for_current_expert_segment = sorted_indices[start_idx_val:end_idx_val]
            actual_token_indices_for_expert = original_token_indices_sorted[start_idx_val:end_idx_val]
            
            if actual_token_indices_for_expert.numel() == 0:
                continue
                
            current_expert_module = self.experts[expert_id]
            expert_input_tokens = x_flat_for_dispatch.index_select(0, actual_token_indices_for_expert)
            expert_output_tokens = current_expert_module(expert_input_tokens)
            
            indices_on_correct_device = indices_for_current_expert_segment
            current_expert_scores = flat_expert_scores.index_select(0, indices_on_correct_device)
            
            expert_output_tokens.mul_(current_expert_scores.unsqueeze(-1))
            expert_output_accumulator.index_add_(0, actual_token_indices_for_expert, expert_output_tokens)
            
        return expert_output_accumulator

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # Restore hidden_dim definition from x.shape
        batch_size, seq_len, hidden_dim_local = x.shape # Fix: Define hidden_dim_local from x.shape
        original_shape = x.shape

        shared_output = self.shared_expert(x)
        expert_indices, expert_scores = self.gating_network(x)

        # Use the locally defined hidden_dim_local
        x_flat = x.view(-1, hidden_dim_local) # Fix: Use hidden_dim_local
        flat_expert_indices = expert_indices.view(-1)
        flat_expert_scores = expert_scores.view(-1)
        
        routed_output_flat = self._moe_dispatch_pytorch_fallback(x_flat, flat_expert_indices, flat_expert_scores)

        routed_output = routed_output_flat.view(original_shape)
        final_output = routed_output + shared_output
        return final_output

def custom_kernel(data: input_t) -> output_t_workaround:
    input_tensor, weights, config = data
    

    moe_model = MoE(config)

    moe_model.gating_network.W_g.weight = nn.Parameter(weights['router.weight'])
    for i in range(config["n_routed_experts"]):
        moe_model.experts[i].W_gate.weight = nn.Parameter(weights[f'experts.{i}.0.weight'].t())
        moe_model.experts[i].W_up.weight = nn.Parameter(weights[f'experts.{i}.1.weight'].t())
        moe_model.experts[i].W_down.weight = nn.Parameter(weights[f'experts.{i}.2.weight'].t())
    moe_model.shared_expert.W_gate.weight = nn.Parameter(weights['shared_experts.0.weight'].t())
    moe_model.shared_expert.W_up.weight = nn.Parameter(weights['shared_experts.1.weight'].t())
    moe_model.shared_expert.W_down.weight = nn.Parameter(weights['shared_experts.2.weight'].t())

    moe_model.eval()
    
    compiled_model = moe_model 
    if hasattr(torch, 'compile'):
        try:
            compiled_model = torch.compile(moe_model, mode="max-autotune")
        except Exception:
            compiled_model = moe_model
    
    with torch.no_grad():
        output_tensor = compiled_model(input_tensor)
    
    return output_tensor
python eval.py test /tmp/tmpix8ac9s5
/usr/local/lib/python3.10/dist-packages/torch/_inductor/lowering.py:7061: UserWarning: 
Online softmax is disabled on the fly since Inductor decides to
split the reduction. Cut an issue to PyTorch if this is an
important use case and you want to speed it up with online
softmax.

  warnings.warn(
AUTOTUNE mm(512x7168, 7168x2048)
strides: [7168, 1], [2048, 1]
dtypes: torch.float16, torch.float16
  mm 0.0653 ms 100.0% 
  triton_mm_72 0.0804 ms 81.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  triton_mm_69 0.0813 ms 80.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  triton_mm_76 0.0817 ms 79.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=4, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  triton_mm_74 0.0823 ms 79.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  triton_mm_77 0.0832 ms 78.5% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=16, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=8
  triton_mm_78 0.0848 ms 76.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=256, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=4, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=8
  triton_mm_86 0.0993 ms 65.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=128, BLOCK_N=64, EVEN_K=True, GROUP_M=16, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  triton_mm_84 0.1013 ms 64.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=128, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  triton_mm_81 0.1115 ms 58.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=4, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=8
SingleProcess AUTOTUNE benchmarking takes 2.5762 seconds and 0.0010 seconds precompiling for 37 choices
AUTOTUNE mm(512x7168, 7168x4)
strides: [7168, 1], [1, 7168]
dtypes: torch.float16, torch.float16
  triton_mm_1 0.0190 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=256, BLOCK_M=32, BLOCK_N=16, EVEN_K=True, GROUP_M=4, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=2
  triton_mm_3 0.0193 ms 98.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=2
  triton_mm_4 0.0235 ms 80.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=2
  triton_mm_5 0.0269 ms 70.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=2, num_stages=2, num_warps=4
  triton_mm_8 0.0269 ms 70.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  triton_mm_11 0.0270 ms 70.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=16, EVEN_K=True, GROUP_M=16, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  triton_mm_14 0.0271 ms 70.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=16, EVEN_K=True, GROUP_M=4, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  triton_mm_12 0.0276 ms 68.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=256, BLOCK_M=64, BLOCK_N=16, EVEN_K=True, GROUP_M=4, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  triton_mm_7 0.0332 ms 57.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  mm 0.0332 ms 57.1% 
SingleProcess AUTOTUNE benchmarking takes 0.2589 seconds and 0.0012 seconds precompiling for 30 choices
AUTOTUNE mm(512x2048, 2048x7168)
strides: [2048, 1], [7168, 1]
dtypes: torch.float16, torch.float16
  mm 0.0411 ms 100.0% 
  triton_mm_129 0.0528 ms 77.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=128, BLOCK_N=128, EVEN_K=True, GROUP_M=16, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=8
  triton_mm_122 0.0538 ms 76.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=128, BLOCK_N=64, EVEN_K=True, GROUP_M=16, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  triton_mm_125 0.0552 ms 74.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=128, BLOCK_N=128, EVEN_K=True, GROUP_M=16, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=8
  triton_mm_115 0.0557 ms 73.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=4, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=2, num_stages=2, num_warps=4
  triton_mm_127 0.0562 ms 73.1% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=128, BLOCK_N=128, EVEN_K=True, GROUP_M=16, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  triton_mm_126 0.0564 ms 72.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=128, BLOCK_N=128, EVEN_K=True, GROUP_M=16, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=1, num_stages=2, num_warps=8
  triton_mm_128 0.0571 ms 71.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=128, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=8
  triton_mm_124 0.0605 ms 67.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=128, BLOCK_N=128, EVEN_K=True, GROUP_M=16, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=2, num_stages=2, num_warps=4
  triton_mm_121 0.0627 ms 65.5% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=128, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=2, num_stages=2, num_warps=4
SingleProcess AUTOTUNE benchmarking takes 4.3150 seconds and 0.0003 seconds precompiling for 37 choices
W0515 11:38:31.124000 356 torch/_dynamo/variables/tensor.py:935] [2/0] Graph break from `Tensor.item()`, consider setting:
W0515 11:38:31.124000 356 torch/_dynamo/variables/tensor.py:935] [2/0]     torch._dynamo.config.capture_scalar_outputs = True
W0515 11:38:31.124000 356 torch/_dynamo/variables/tensor.py:935] [2/0] or:
W0515 11:38:31.124000 356 torch/_dynamo/variables/tensor.py:935] [2/0]     env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
W0515 11:38:31.124000 356 torch/_dynamo/variables/tensor.py:935] [2/0] to include these operations in the captured graph.
W0515 11:38:31.124000 356 torch/_dynamo/variables/tensor.py:935] [2/0] 
W0515 11:38:31.124000 356 torch/_dynamo/variables/tensor.py:935] [2/0] Graph break: from user code at:
W0515 11:38:31.124000 356 torch/_dynamo/variables/tensor.py:935] [2/0]   File "/home/runner/_work/discord-cluster-manager/discord-cluster-manager/submission.py", line 76, in torch_dynamo_resume_in__moe_dispatch_pytorch_fallback_at_69
W0515 11:38:31.124000 356 torch/_dynamo/variables/tensor.py:935] [2/0]     start_idx_val = start_idx_tensor.item()
W0515 11:38:31.124000 356 torch/_dynamo/variables/tensor.py:935] [2/0] 
W0515 11:38:31.124000 356 torch/_dynamo/variables/tensor.py:935] [2/0] 
AUTOTUNE mm(1024x7168, 7168x2048)
strides: [7168, 1], [2048, 1]
dtypes: torch.float16, torch.float16
  mm 0.0900 ms 100.0% 
  triton_mm_331 0.1192 ms 75.5% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=128, BLOCK_N=64, EVEN_K=True, GROUP_M=16, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  triton_mm_321 0.1200 ms 75.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=4, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  triton_mm_326 0.1202 ms 74.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=4, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=8
  triton_mm_327 0.1235 ms 72.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=4, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=8
  triton_mm_332 0.1289 ms 69.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=128, BLOCK_N=64, EVEN_K=True, GROUP_M=4, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=8
  triton_mm_325 0.1293 ms 69.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=8
  triton_mm_338 0.1321 ms 68.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=128, BLOCK_N=128, EVEN_K=True, GROUP_M=16, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=8
  triton_mm_329 0.1337 ms 67.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=128, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  triton_mm_324 0.1368 ms 65.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=4, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=2, num_stages=2, num_warps=4
SingleProcess AUTOTUNE benchmarking takes 4.5775 seconds and 0.1012 seconds precompiling for 37 choices
AUTOTUNE mm(1024x7168, 7168x8)
strides: [7168, 1], [1, 7168]
dtypes: torch.float16, torch.float16
  triton_mm_248 0.0196 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=2
  triton_mm_246 0.0221 ms 88.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=256, BLOCK_M=32, BLOCK_N=16, EVEN_K=True, GROUP_M=4, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=2
  triton_mm_249 0.0241 ms 81.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=2
  mm 0.0262 ms 74.6% 
  triton_mm_250 0.0273 ms 71.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=2, num_stages=2, num_warps=4
  triton_mm_253 0.0273 ms 71.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  triton_mm_256 0.0276 ms 70.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=16, EVEN_K=True, GROUP_M=16, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  triton_mm_257 0.0283 ms 69.1% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=256, BLOCK_M=64, BLOCK_N=16, EVEN_K=True, GROUP_M=4, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  triton_mm_259 0.0287 ms 68.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=16, EVEN_K=True, GROUP_M=4, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  triton_mm_252 0.0334 ms 58.5% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
SingleProcess AUTOTUNE benchmarking takes 3.5040 seconds and 0.0002 seconds precompiling for 30 choices
AUTOTUNE mm(1024x2048, 2048x7168)
strides: [2048, 1], [7168, 1]
dtypes: torch.float16, torch.float16
  mm 0.0702 ms 100.0% 
  triton_mm_380 0.0844 ms 83.1% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=256, BLOCK_N=128, EVEN_K=True, GROUP_M=4, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=8
  triton_mm_376 0.0850 ms 82.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=128, BLOCK_N=256, EVEN_K=True, GROUP_M=4, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=8
  triton_mm_369 0.0880 ms 79.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=128, BLOCK_N=128, EVEN_K=True, GROUP_M=16, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=2, num_stages=2, num_warps=4
  triton_mm_360 0.0903 ms 77.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=4, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=2, num_stages=2, num_warps=4
  triton_mm_379 0.0907 ms 77.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=256, BLOCK_N=128, EVEN_K=True, GROUP_M=16, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=8
  triton_mm_366 0.0908 ms 77.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=128, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=2, num_stages=2, num_warps=4
  triton_mm_378 0.0916 ms 76.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=256, BLOCK_N=128, EVEN_K=True, GROUP_M=4, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=2, num_stages=2, num_warps=4
  triton_mm_375 0.0917 ms 76.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=128, BLOCK_N=256, EVEN_K=True, GROUP_M=16, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=2, num_stages=2, num_warps=4
  triton_mm_373 0.0992 ms 70.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=128, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=8
SingleProcess AUTOTUNE benchmarking takes 4.4310 seconds and 0.0003 seconds precompiling for 37 choices
AUTOTUNE mm(536x7168, 7168x2048)
strides: [7168, 1], [2048, 1]
dtypes: torch.float16, torch.float16
  triton_mm_422 0.0838 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=4
  triton_mm_430 0.0843 ms 99.5% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=16, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=8
  triton_mm_431 0.0886 ms 94.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=256, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=4, USE_FAST_ACCUM=False, kpack=2, matrix_instr_nonkdim=16, waves_per_eu=0, num_stages=2, num_warps=8
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