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/tmpm1zkqr6g
/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 0.0675 ms 100.0% 
  triton_mm_72 0.0807 ms 83.7% 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 0.0812 ms 83.2% 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_77 0.0812 ms 83.2% 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_76 0.0816 ms 82.8% 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_78 0.0849 ms 79.5% 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_74 0.0850 ms 79.4% 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_84 0.0963 ms 70.1% 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 0.0993 ms 68.0% 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 0.1113 ms 60.7% 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 2.1696 seconds and 0.0011 seconds precompiling for 37 choices
AUTOTUNE mm(512x7168, 7168x4)
strides: [7168, 1], [1, 7168]
dtypes: torch.float16, torch.float16
  triton_mm_1 0.0199 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 0.0206 ms 96.5% 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 0.0239 ms 83.2% 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_14 0.0270 ms 73.8% 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_11 0.0271 ms 73.6% 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_5 0.0271 ms 73.5% 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 0.0274 ms 72.8% 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_12 0.0275 ms 72.4% 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_10 0.0331 ms 60.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, 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 0.0333 ms 59.8% 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 0.2633 seconds and 0.0013 seconds precompiling for 30 choices
AUTOTUNE mm(512x2048, 2048x7168)
strides: [2048, 1], [7168, 1]
dtypes: torch.float16, torch.float16
  mm 0.0420 ms 100.0% 
  triton_mm_129 0.0515 ms 81.6% 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_122 0.0528 ms 79.5% 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_115 0.0540 ms 77.9% 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_126 0.0558 ms 75.3% 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 0.0564 ms 74.5% 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_127 0.0568 ms 73.9% 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_125 0.0574 ms 73.2% 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=0, num_stages=2, num_warps=8
  triton_mm_124 0.0604 ms 69.5% 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 0.0623 ms 67.5% 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 4.2930 seconds and 0.0003 seconds precompiling for 37 choices
W0515 11:13:11.065000 405 torch/_dynamo/variables/tensor.py:935] [2/0] Graph break from `Tensor.item()`, consider setting:
W0515 11:13:11.065000 405 torch/_dynamo/variables/tensor.py:935] [2/0]     torch._dynamo.config.capture_scalar_outputs = True
W0515 11:13:11.065000 405 torch/_dynamo/variables/tensor.py:935] [2/0] or:
W0515 11:13:11.065000 405 torch/_dynamo/variables/tensor.py:935] [2/0]     env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
W0515 11:13:11.065000 405 torch/_dynamo/variables/tensor.py:935] [2/0] to include these operations in the captured graph.
W0515 11:13:11.065000 405 torch/_dynamo/variables/tensor.py:935] [2/0] 
W0515 11:13:11.065000 405 torch/_dynamo/variables/tensor.py:935] [2/0] Graph break: from user code at:
W0515 11:13:11.065000 405 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 11:13:11.065000 405 torch/_dynamo/variables/tensor.py:935] [2/0]     start_idx_val = start_idx_tensor.item()
W0515 11:13:11.065000 405 torch/_dynamo/variables/tensor.py:935] [2/0] 
W0515 11:13:11.065000 405 torch/_dynamo/variables/tensor.py:935] [2/0] 
AUTOTUNE mm(1024x7168, 7168x2048)
strides: [7168, 1], [2048, 1]
dtypes: torch.float16, torch.float16
  mm 0.0955 ms 100.0% 
  triton_mm_331 0.1107 ms 86.3% 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 0.1172 ms 81.5% 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 0.1206 ms 79.2% 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 0.1244 ms 76.8% 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_338 0.1301 ms 73.4% 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_332 0.1305 ms 73.2% 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 0.1307 ms 73.0% 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_329 0.1341 ms 71.2% 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_324 0.1400 ms 68.2% 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 4.6356 seconds and 0.0973 seconds precompiling for 37 choices
AUTOTUNE mm(1024x7168, 7168x8)
strides: [7168, 1], [1, 7168]
dtypes: torch.float16, torch.float16
  triton_mm_248 0.0206 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 0.0220 ms 93.6% 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 0.0237 ms 86.9% 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_250 0.0273 ms 75.2% 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_256 0.0275 ms 74.8% 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 0.0275 ms 74.7% 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_259 0.0276 ms 74.5% 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 0.0283 ms 72.6% 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_252 0.0335 ms 61.4% 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
  triton_mm_255 0.0336 ms 61.1% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, 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
SingleProcess AUTOTUNE benchmarking takes 3.4931 seconds and 0.0003 seconds precompiling for 30 choices
AUTOTUNE mm(1024x2048, 2048x7168)
strides: [2048, 1], [7168, 1]
dtypes: torch.float16, torch.float16
  mm 0.0655 ms 100.0% 
  triton_mm_380 0.0828 ms 79.1% 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 77.1% 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 0.0858 ms 76.4% 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_366 0.0884 ms 74.1% 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_360 0.0898 ms 72.9% 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_378 0.0904 ms 72.5% 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 0.0941 ms 69.6% 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_379 0.0949 ms 69.1% 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_371 0.0987 ms 66.4% 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=1, num_stages=2, num_warps=8
SingleProcess AUTOTUNE benchmarking takes 4.4562 seconds and 0.0003 seconds precompiling for 37 choices
AUTOTUNE mm(481x7168, 7168x2048)
strides: [7168, 1], [2048, 1]
dtypes: torch.float16, torch.float16
  mm 0.0647 ms 100.0% 
  triton_mm_425 0.0819 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
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