id stringlengths 3 38 | prompt stringlengths 4.64k 7.76k | ref_time float64 0 5.24 |
|---|---|---|
min_gpt_new_gelu | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.012471 |
hardsigmoid | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.001713 |
sigmoid | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.000255 |
leaky_relu | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.00007 |
tanh | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.000225 |
selu | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.00038 |
hardtanh | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.000232 |
softsign | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.000212 |
elu | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.000309 |
relu | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.000157 |
swish | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.000284 |
softmax | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.000202 |
log_softmax | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.000217 |
index_select | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.001389 |
scatter | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.000196 |
index_copy | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.009264 |
take_along_dim | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.001166 |
argmax_over_a_dimension | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.00056 |
argmin_over_a_dimension | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.000525 |
masked_fill | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.021462 |
embedding | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.055533 |
index_add | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.031631 |
scatter_add | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.001787 |
inplace_update | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.000243 |
triplet_margin_loss | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.002249 |
huber_loss | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.00026 |
kl_div_loss | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.001168 |
cosine_similarity_loss | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.000554 |
mse_loss | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.000209 |
cross_entropy_loss | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.000187 |
cumsum_exclusive | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 0.000426 |
masked_cumsum | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 0.000278 |
matrix_scalar_multiplication | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 0.071325 |
cumprod | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 0.007875 |
cumsum_reverse | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 0.000323 |
matmul_with_small_k_dimension | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 0.362994 |
matmul_with_transposed_both | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 0.11127 |
matrix_vector_multiplication | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 0.002103 |
four_dim_tensor_matrix_multiplication | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 5.236348 |
matmul_with_transposed_b | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 0.278551 |
matmul_with_large_k_dimension | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 0.065918 |
matmul_for_lower_triangular_matrices | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 0.505047 |
matmul_with_irregular_shapes | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 1.171759 |
batched_matrix_multiplication | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 0.319429 |
matmul_for_symmetric_matrices | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 0.645905 |
standard_matrix_multiplication | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 0.088071 |
square_matrix_multiplication | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 0.082542 |
tall_skinny_matrix_multiplication | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 0.421983 |
matmul_for_upper_triangular_matrices | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 0.582953 |
matmul_with_diagonal_matrices | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 0.582444 |
three_dim_tensor_matrix_multiplication | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python... | 0.186669 |
instance_norm | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```py... | 0.110181 |
l2_norm | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```py... | 0.010375 |
rms_norm | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```py... | 0.205776 |
layer_norm | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```py... | 0.113798 |
batch_norm | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```py... | 0.115461 |
frobenius_norm | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```py... | 0.164778 |
group_norm | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```py... | 0.105444 |
adam | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.021668 |
adagrad | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.009261 |
sgd | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.005109 |
rmsprop | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```pytho... | 0.013808 |
max_pooling_3d | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python... | 0.095511 |
max_pooling_1d | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python... | 0.002416 |
average_pooling_1d | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python... | 0.000181 |
average_pooling_3d | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python... | 0.052774 |
average_pooling_2d | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python... | 0.017813 |
sum_reduction_over_a_dimension | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```py... | 0.000232 |
max_reduction_over_a_dimension | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```py... | 0.00022 |
mean_reduction_over_a_dimension | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```py... | 0.000304 |
product_reduction_over_a_dimension | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```py... | 0.000217 |
upsample_grid_sample | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python... | 0.013539 |
nearest_neighbor_upsample | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python... | 0.048744 |
grid_sample_affine | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python... | 0.029867 |
grid_sample_random_warp | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python... | 0.003833 |
bicubic_upsample | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python... | 0.026305 |
downsample_bilinear | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python... | 0.004658 |
resize_with_antialias | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python... | 0.001044 |
interpolate_dynamic | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python... | 0.0468 |
trilinear_upsample | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python... | 0.017522 |
bilinear_upsample | You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python... | 0.050005 |
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