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_spec = TypeVar("output_t_spec", bound=Tuple[torch.Tensor, Dict]) # Original spec from task.yml
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:
        target_device = x_flat_for_dispatch.device # Define the target device once
        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):
            # Convert tensor elements to Python int for slicing if they are not already
            # This ensures start_idx_val and end_idx_val are Python scalars.
            start_idx_tensor = expert_dispatch_boundaries[expert_id-1] if expert_id > 0 else torch.tensor(0, device=target_device)
            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)
            
            # --------- Attempted fix for device mismatch --------
            # Ensure indices_for_current_expert_segment is on the same device as flat_expert_scores
            # This tensor should already be on target_device because sorted_indices is.
            # Adding an explicit .to() here is a defensive measure.
            indices_on_correct_device = indices_for_current_expert_segment # .to(target_device) -> already a slice of a tensor on target_device

            # The error occurs here:
            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:
        batch_size, seq_len, hidden_dim = x.shape
        original_shape = x.shape

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

        x_flat = x.view(-1, hidden_dim)
        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
    device = input_tensor.device

    moe_model = MoE(config).to(device)

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

    moe_model.eval()
    
    compiled_model = moe_model 
    if hasattr(torch, 'compile'):
        try:
            compiled_model = torch.compile(moe_model, mode="max-autotune")
            # print("Successfully compiled MoE model with torch.compile(mode='max-autotune').") 
        except Exception as e:
            # print(f"Torch.compile failed: {e}. Falling back to eager mode.")
            compiled_model = moe_model 
    # else:
        # print("torch.compile not available. Using eager mode.")
    
    with torch.no_grad():
        output_tensor = compiled_model(input_tensor)
    
    return output_tensor
python eval.py test /tmp/tmp0vyq_5hm
/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 0.0659 ms 100.0% 
  triton_mm_69 0.0804 ms 81.9% 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 0.0823 ms 80.0% 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_74 0.0833 ms 79.0% 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_72 0.0834 ms 79.0% 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_78 0.0851 ms 77.4% 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_77 0.0852 ms 77.4% 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_84 0.0993 ms 66.3% 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_86 0.0998 ms 66.0% 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_81 0.1119 ms 58.8% 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 2.0523 seconds and 0.0009 seconds precompiling for 37 choices
AUTOTUNE mm(512x7168, 7168x4)
strides: [7168, 1], [1, 7168]
dtypes: torch.float16, torch.float16
  triton_mm_1 0.0189 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 0.0192 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 0.0231 ms 81.6% 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 0.0269 ms 70.2% 
  triton_mm_5 0.0270 ms 70.0% 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_14 0.0271 ms 69.8% 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_8 0.0271 ms 69.6% 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 0.0271 ms 69.6% 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_12 0.0277 ms 68.3% 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 0.0334 ms 56.5% 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 0.2510 seconds and 0.0013 seconds precompiling for 30 choices
AUTOTUNE mm(512x2048, 2048x7168)
strides: [2048, 1], [7168, 1]
dtypes: torch.float16, torch.float16
  mm 0.0412 ms 100.0% 
  triton_mm_122 0.0518 ms 79.6% 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_129 0.0524 ms 78.6% 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_126 0.0565 ms 73.0% 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_125 0.0565 ms 72.9% 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=0, num_stages=2, num_warps=8
  triton_mm_127 0.0566 ms 72.9% 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_115 0.0568 ms 72.5% 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_128 0.0578 ms 71.3% 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 0.0616 ms 66.9% 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 0.0631 ms 65.3% 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
SingleProcess AUTOTUNE benchmarking takes 4.3016 seconds and 0.0003 seconds precompiling for 37 choices
W0515 11:13:00.015000 389 torch/_dynamo/variables/tensor.py:935] [2/0] Graph break from `Tensor.item()`, consider setting:
W0515 11:13:00.015000 389 torch/_dynamo/variables/tensor.py:935] [2/0]     torch._dynamo.config.capture_scalar_outputs = True
W0515 11:13:00.015000 389 torch/_dynamo/variables/tensor.py:935] [2/0] or:
W0515 11:13:00.015000 389 torch/_dynamo/variables/tensor.py:935] [2/0]     env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
W0515 11:13:00.015000 389 torch/_dynamo/variables/tensor.py:935] [2/0] to include these operations in the captured graph.
W0515 11:13:00.015000 389 torch/_dynamo/variables/tensor.py:935] [2/0] 
W0515 11:13:00.015000 389 torch/_dynamo/variables/tensor.py:935] [2/0] Graph break: from user code at:
W0515 11:13:00.015000 389 torch/_dynamo/variables/tensor.py:935] [2/0]   File "/home/runner/_work/discord-cluster-manager/discord-cluster-manager/submission.py", line 80, in torch_dynamo_resume_in__moe_dispatch_pytorch_fallback_at_71
W0515 11:13:00.015000 389 torch/_dynamo/variables/tensor.py:935] [2/0]     start_idx_val = start_idx_tensor.item()
W0515 11:13:00.015000 389 torch/_dynamo/variables/tensor.py:935] [2/0] 
W0515 11:13:00.015000 389 torch/_dynamo/variables/tensor.py:935] [2/0] 
AUTOTUNE mm(1024x7168, 7168x2048)
strides: [7168, 1], [2048, 1]
dtypes: torch.float16, torch.float16
  mm 0.0926 ms 100.0% 
  triton_mm_331 0.1133 ms 81.7% 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_326 0.1182 ms 78.3% 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_321 0.1215 ms 76.2% 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_327 0.1243 ms 74.5% 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 0.1252 ms 74.0% 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_329 0.1319 ms 70.2% 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_338 0.1320 ms 70.1% 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_325 0.1324 ms 69.9% 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_324 0.1409 ms 65.7% 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
SingleProcess AUTOTUNE benchmarking takes 4.5914 seconds and 0.1093 seconds precompiling for 37 choices
AUTOTUNE mm(1024x7168, 7168x8)
strides: [7168, 1], [1, 7168]
dtypes: torch.float16, torch.float16
  triton_mm_248 0.0194 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 0.0215 ms 89.9% 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 0.0234 ms 82.7% 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
  mm 0.0255 ms 75.9% 
  triton_mm_256 0.0273 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_253 0.0274 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_250 0.0275 ms 70.3% 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_259 0.0276 ms 70.2% 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_257 0.0281 ms 68.9% 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_252 0.0337 ms 57.4% 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 3.4986 seconds and 0.0002 seconds precompiling for 30 choices
AUTOTUNE mm(1024x2048, 2048x7168)
strides: [2048, 1], [7168, 1]
dtypes: torch.float16, torch.float16
  mm 0.0666 ms 100.0% 
  triton_mm_369 0.0849 ms 78.4% 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=2, num_stages=2, num_warps=4
  triton_mm_380 0.0858 ms 77.6% 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 0.0878 ms 75.8% 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_378 0.0919 ms 72.4% 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 0.0954 ms 69.8% 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_379 0.0954 ms 69.7% 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 0.0966 ms 68.9% 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_371 0.0991 ms 67.2% 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_360 0.1007 ms 66.1% 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 4.4257 seconds and 0.0003 seconds precompiling for 37 choices
AUTOTUNE mm(536x7168, 7168x2048)
strides: [7168, 1], [2048, 1]
dtypes: torch.float16, torch.float16
  mm 0.0564 ms 100.0% 
  triton_mm_422 0.0857 ms 65.8% 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_425 0.0867 ms 65.0% 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_427 0.0879 ms 64.1% 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
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