pmpp v2 #48
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pmpp v2 #48
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S1ro1
commented
Jul 17, 2025
S1ro1
commented
- Feat: grayscale
- Feat: histogram
- Feat: matmul
- Prefixsum
- Feat: sort
- Vectoradd
- Feat: vectoradd
- Feat: conv2d
- Fix: task.py
- Feat: generate_inputs
ngc92
reviewed
Jul 17, 2025
| input_tensor, kernel = data | ||
| return torch.empty((input_tensor.shape[0], input_tensor.shape[1], input_tensor.shape[2]-kernel.shape[3]+1, input_tensor.shape[3]-kernel.shape[3]+1), | ||
| device=kernel.device, dtype=kernel.dtype | ||
| input_tensor, kernel, output = data |
| input_tensor, kernel, output = data |
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add test submission doing conf with tf32 / fp16; decide on whether we want this to pass or fail
| @@ -4,4 +4,5 @@ | |||
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| def custom_kernel(data: input_t) -> output_t: | |||
| data, _output = data | |||
| return torch.empty(size=(data.shape[0], data.shape[1]), device=data.device, dtype=data.dtype) | |||
| @@ -3,4 +3,6 @@ | |||
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| def custom_kernel(data: input_t) -> output_t: | |||
| return torch.bincount(data, minlength=256) | |||
| data, output = data | |||
| output = torch.bincount(data, minlength=256) | |||
| @@ -4,4 +4,5 @@ | |||
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| def custom_kernel(data: input_t) -> output_t: | |||
| data, _output = data | |||
| return torch.empty(size=(256,), device=data.device, dtype=data.dtype) | |||
| @@ -3,4 +3,6 @@ | |||
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| def custom_kernel(data: input_t) -> output_t: | |||
| return torch.cumsum(data, dim=0) | |||
| output, data = data | |||
| output = torch.cumsum(data, dim=0) | |||
| @@ -4,4 +4,6 @@ | |||
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| def custom_kernel(data: input_t) -> output_t: | |||
| return torch.empty(size=data.shape, device=data.device, dtype=data.dtype) | |||
| data, output = data | |||
| output = torch.empty(size=data.shape, device=data.device, dtype=data.dtype) | |||
| data, output = data | ||
| output = torch.cumsum(data, dim=0) |
| @@ -11,37 +11,48 @@ def ref_kernel(data: input_t) -> output_t: | |||
| Returns: | |||
| Sorted tensor | |||
| """ | |||
| return torch.sort(data)[0] | |||
| data, output = data | |||
| output = torch.sort(data)[0] | |||
| @@ -5,7 +5,9 @@ | |||
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| def _custom_kernel(data: input_t) -> output_t: | |||
| return data.sum() | |||
| data, output = data | |||
| output = data.sum() | |||
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