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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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name min_gpt_new_gelu
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
# From https://github.com/karpathy/minGPT/blob/master/mingpt/model.py
class Model(nn.Module):
"""
Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT).
Reference: Gaussian Error Linear Units (GELU) paper: https://arxiv.org/abs/1606.08415
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x):
return 0.5 * x * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name hardsigmoid
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a HardSigmoid activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies HardSigmoid activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of any shape.
Returns:
torch.Tensor: Output tensor with HardSigmoid applied, same shape as input.
"""
return torch.nn.functional.hardsigmoid(x)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name sigmoid
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Sigmoid activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Sigmoid activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of any shape.
Returns:
torch.Tensor: Output tensor with Sigmoid applied, same shape as input.
"""
return torch.sigmoid(x)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name leaky_relu
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a LeakyReLU activation.
"""
def __init__(self, negative_slope: float = 0.01):
"""
Initializes the LeakyReLU module.
Args:
negative_slope (float, optional): The negative slope of the activation function. Defaults to 0.01.
"""
super(Model, self).__init__()
self.negative_slope = negative_slope
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies LeakyReLU activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of any shape.
Returns:
torch.Tensor: Output tensor with LeakyReLU applied, same shape as input.
"""
return torch.nn.functional.leaky_relu(x, negative_slope=self.negative_slope)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name tanh
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Tanh activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Tanh activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of any shape.
Returns:
torch.Tensor: Output tensor with Tanh applied, same shape as input.
"""
return torch.tanh(x)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name selu
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a SELU activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies SELU activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of any shape.
Returns:
torch.Tensor: Output tensor with SELU applied, same shape as input.
"""
return torch.selu(x)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name hardtanh
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Simple model that performs a HardTanh activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies HardTanh activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of any shape.
Returns:
torch.Tensor: Output tensor with HardTanh applied, same shape as input.
"""
return F.hardtanh(x, min_val=-1., max_val=1.)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name softsign
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softsign activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softsign activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of any shape.
Returns:
torch.Tensor: Output tensor with Softsign applied, same shape as input.
"""
return x / (1 + torch.abs(x))
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name elu
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Simple model that performs an ELU activation.
"""
def __init__(self, alpha: float = 1.0):
"""
Initializes the ELU model.
Args:
alpha (float, optional): The alpha parameter for the ELU function. Defaults to 1.0.
"""
super(Model, self).__init__()
self.alpha = alpha
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies ELU activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of any shape.
Returns:
torch.Tensor: Output tensor with ELU applied, same shape as input.
"""
return F.elu(x, alpha=self.alpha)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name relu
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a ReLU activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies ReLU activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of any shape.
Returns:
torch.Tensor: Output tensor with ReLU applied, same shape as input.
"""
return torch.relu(x)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name swish
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Swish activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Swish activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of any shape.
Returns:
torch.Tensor: Output tensor with Swish applied, same shape as input.
"""
return x * torch.sigmoid(x)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name softmax
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name log_softmax
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a LogSoftmax activation.
"""
def __init__(self, dim: int = 1):
super(Model, self).__init__()
self.dim = dim
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies LogSoftmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, dim).
Returns:
torch.Tensor: Output tensor with LogSoftmax applied, same shape as input.
"""
return torch.log_softmax(x, dim=self.dim)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name index_select
```python
import torch
import torch.nn as nn
class Model(nn.Module):
def forward(self, x, indices):
return torch.index_select(x, dim=1, index=indices)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name scatter
```python
import torch
import torch.nn as nn
class Model(nn.Module):
def forward(self, x, idx, updates):
return x.scatter(dim=1, index=idx, src=updates)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name index_copy
```python
import torch
import torch.nn as nn
class Model(nn.Module):
def forward(self, x, indices, src):
return x.index_copy(0, indices, src)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name take_along_dim
```python
import torch
import torch.nn as nn
class Model(nn.Module):
def forward(self, x, idx):
return torch.take_along_dim(x, idx, dim=1)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name argmax_over_a_dimension
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs Argmax over a specified dimension.
"""
def __init__(self, dim: int):
"""
Initializes the model with the dimension to perform argmax.
Args:
dim (int): The dimension to perform argmax over.
"""
super(Model, self).__init__()
self.dim = dim
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies argmax over the specified dimension to the input tensor.
Args:
x (torch.Tensor): Input tensor.
Returns:
torch.Tensor: Output tensor with argmax applied, with the specified dimension removed.
"""
return torch.argmax(x, dim=self.dim)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name argmin_over_a_dimension
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that finds the index of the minimum value along a specified dimension.
"""
def __init__(self, dim: int):
"""
Initializes the model with the dimension to perform argmin on.
Args:
dim (int): Dimension along which to find the minimum value.
"""
super(Model, self).__init__()
self.dim = dim
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Finds the index of the minimum value along the specified dimension.
Args:
x (torch.Tensor): Input tensor.
Returns:
torch.Tensor: Tensor containing the indices of the minimum values along the specified dimension.
"""
return torch.argmin(x, dim=self.dim)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name masked_fill
```python
import torch
import torch.nn as nn
class Model(nn.Module):
def forward(self, x, mask):
return x.masked_fill(mask, float('-inf'))
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name embedding
```python
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.embedding = nn.Embedding(100000, 768)
def forward(self, indices):
return self.embedding(indices)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name index_add
```python
import torch
import torch.nn as nn
class Model(nn.Module):
def forward(self, x, indices, values):
return x.index_add(dim=0, index=indices, source=values)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name scatter_add
```python
import torch
import torch.nn as nn
class Model(nn.Module):
def forward(self, x, idx, updates):
return x.scatter_add(dim=1, index=idx, src=updates)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name inplace_update
```python
import torch
import torch.nn as nn
class Model(nn.Module):
def forward(self, x, idx, value):
x[idx] = value
return x
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name triplet_margin_loss
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
A model that computes Triplet Margin Loss for metric learning tasks.
Parameters:
margin (float): The margin between the positive and negative samples.
"""
def __init__(self, margin=1.0):
super(Model, self).__init__()
self.loss_fn = torch.nn.TripletMarginLoss(margin=margin)
def forward(self, anchor, positive, negative):
return self.loss_fn(anchor, positive, negative)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name huber_loss
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
A model that computes Smooth L1 (Huber) Loss for regression tasks.
Parameters:
None
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, predictions, targets):
return torch.nn.functional.smooth_l1_loss(predictions, targets)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name kl_div_loss
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
A model that computes Kullback-Leibler Divergence for comparing two distributions.
Parameters:
None
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, predictions, targets):
return torch.nn.functional.kl_div(torch.log(predictions), targets, reduction='batchmean')
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name cosine_similarity_loss
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
A model that computes Cosine Similarity Loss for comparing vectors.
Parameters:
None
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, predictions, targets):
cosine_sim = torch.nn.functional.cosine_similarity(predictions, targets, dim=1)
return torch.mean(1 - cosine_sim)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name mse_loss
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
A model that computes the Mean Squared Error loss for regression tasks.
Parameters:
None
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, predictions, targets):
return torch.mean((predictions - targets) ** 2)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.softmax(x, dim=1)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name softmax**:
The transformtion includes three parts: `softmax_kernel` function, `softmax` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
@triton.jit
def softmax_kernel(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, BLOCK_SIZE: tl.constexpr):
# Starting row for this program
row_start = tl.program_id(0)
row_step = tl.num_programs(0)
for row_idx in tl.range(row_start, n_rows, row_step):
# Row stride indicates how much to advance the pointer per row
row_start_ptr = input_ptr + row_idx * input_row_stride
# Block size is the next power of 2 greater than n_cols
# to fit a single row within a block
col_offsets = tl.arange(0, BLOCK_SIZE)
input_ptrs = row_start_ptr + col_offsets
# Load the row into SRAM, use mask since BLOCK_SIZE may exceed n_cols
mask = col_offsets < n_cols
row = tl.load(input_ptrs, mask=mask, other=-float('inf'))
# Subtract max value for numerical stability
row_minus_max = row - tl.max(row, axis=0)
# Note: exponential in Triton is fast but approximate
numerator = tl.exp(row_minus_max)
denominator = tl.sum(numerator, axis=0)
softmax_output = numerator / denominator
# Write output back to DRAM
output_row_start_ptr = output_ptr + row_idx * output_row_stride
output_ptrs = output_row_start_ptr + col_offsets
tl.store(output_ptrs, softmax_output, mask=mask)
kernels = {}
@torch.inference_mode()
def softmax(x):
n_rows, n_cols = x.shape
# Block size for each iteration is the smallest power of 2 greater than the number of columns in x
BLOCK_SIZE = triton.next_power_of_2(n_cols)
# Allocate output tensor
y = torch.empty_like(x)
# Precompile kernel to get register usage and calculate thread occupancy
kernel, num_programs = kernels.get(BLOCK_SIZE, (None, 0))
if kernel is None:
num_programs = 32
kernel = softmax_kernel
kernels[BLOCK_SIZE] = (kernel, num_programs)
num_programs = min(num_programs, n_rows)
kernel[(num_programs, 1, 1)](
y,
x,
x.stride(0),
y.stride(0),
n_rows,
n_cols,
BLOCK_SIZE
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs a Softmax activation.
"""
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Softmax activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return softmax(x)
```
Now, you are given the following PyTorch architecture with name cross_entropy_loss
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
A model that computes Cross Entropy Loss for multi-class classification tasks.
Parameters:
None
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, predictions, targets):
return torch.nn.functional.cross_entropy(predictions, targets)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
"""
Applies Matrix Multiplication to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (M, K).
x (torch.Tensor): Input tensor of shape (K, N).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.matmul(x, y)
M, K, N = 64, 32, 128
def get_inputs():
x = torch.randn(M, K)
y = torch.randn(K, N)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name matmul**:
The transformtion includes three parts: `matmul_kernel` function, `matmul` afunction, and `ModelNew` class.
```python
import torch
import torch_npu
import torch.nn as nn
import triton
import triton.language as tl
import time
@triton.jit
def matmul_kernel(
a_ptr, b_ptr, c_ptr,
M, N, K,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr
):
"""Simplified matmul kernel without GROUP_SIZE_M optimization."""
# Each program computes one block in (M, N) grid
pid = tl.program_id(axis=0)
num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
pid_m = pid // num_pid_n
pid_n = pid % num_pid_n
# Compute offsets
offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
# Initialize accumulator
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
accumulator = tl.dot(a, b, accumulator)
a_ptrs += BLOCK_SIZE_K * stride_ak
b_ptrs += BLOCK_SIZE_K * stride_bk
c = accumulator.to(tl.float32)
# Write back with mask
offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
tl.store(c_ptrs, c, mask=c_mask)
def matmul(x, y):
M, K = x.shape
K, N = y.shape
output = torch.empty((M, N), device=x.device, dtype=torch.float32)
grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']), )
matmul_kernel[grid](
x, y, output, #
M, N, K, #
x.stride(0), x.stride(1), #
y.stride(0), y.stride(1), #
output.stride(0), output.stride(1), #
BLOCK_SIZE_M=16, BLOCK_SIZE_N=16, BLOCK_SIZE_K=8
)
return output
class ModelNew(nn.Module):
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
return matmul(x, y)
```
Now, you are given the following PyTorch architecture with name cumsum_exclusive
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
A model that performs an exclusive cumulative sum (does not include the current element).
Parameters:
dim (int): The dimension along which to perform the exclusive cumulative sum.
"""
def __init__(self, dim):
super(Model, self).__init__()
self.dim = dim
def forward(self, x):
exclusive_cumsum = torch.cat((torch.zeros_like(x.select(self.dim, 0).unsqueeze(self.dim)), x), dim=self.dim)[:-1]
return torch.cumsum(exclusive_cumsum, dim=self.dim)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
"""
Applies Matrix Multiplication to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (M, K).
x (torch.Tensor): Input tensor of shape (K, N).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.matmul(x, y)
M, K, N = 64, 32, 128
def get_inputs():
x = torch.randn(M, K)
y = torch.randn(K, N)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name matmul**:
The transformtion includes three parts: `matmul_kernel` function, `matmul` afunction, and `ModelNew` class.
```python
import torch
import torch_npu
import torch.nn as nn
import triton
import triton.language as tl
import time
@triton.jit
def matmul_kernel(
a_ptr, b_ptr, c_ptr,
M, N, K,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr
):
"""Simplified matmul kernel without GROUP_SIZE_M optimization."""
# Each program computes one block in (M, N) grid
pid = tl.program_id(axis=0)
num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
pid_m = pid // num_pid_n
pid_n = pid % num_pid_n
# Compute offsets
offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
# Initialize accumulator
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
accumulator = tl.dot(a, b, accumulator)
a_ptrs += BLOCK_SIZE_K * stride_ak
b_ptrs += BLOCK_SIZE_K * stride_bk
c = accumulator.to(tl.float32)
# Write back with mask
offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
tl.store(c_ptrs, c, mask=c_mask)
def matmul(x, y):
M, K = x.shape
K, N = y.shape
output = torch.empty((M, N), device=x.device, dtype=torch.float32)
grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']), )
matmul_kernel[grid](
x, y, output, #
M, N, K, #
x.stride(0), x.stride(1), #
y.stride(0), y.stride(1), #
output.stride(0), output.stride(1), #
BLOCK_SIZE_M=16, BLOCK_SIZE_N=16, BLOCK_SIZE_K=8
)
return output
class ModelNew(nn.Module):
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
return matmul(x, y)
```
Now, you are given the following PyTorch architecture with name masked_cumsum
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
A model that performs a masked cumulative sum, only summing elements that satisfy a condition.
Parameters:
dim (int): The dimension along which to perform the masked cumulative sum.
"""
def __init__(self, dim):
super(Model, self).__init__()
self.dim = dim
def forward(self, x, mask):
"""
Args:
x (torch.Tensor): Input tensor of shape (batch_size, *input_shape).
mask (torch.Tensor): Boolean mask of the same shape as x.
Returns:
torch.Tensor: Cumulative sum of elements where mask is True.
"""
return torch.cumsum(x * mask, dim=self.dim)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
"""
Applies Matrix Multiplication to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (M, K).
x (torch.Tensor): Input tensor of shape (K, N).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.matmul(x, y)
M, K, N = 64, 32, 128
def get_inputs():
x = torch.randn(M, K)
y = torch.randn(K, N)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name matmul**:
The transformtion includes three parts: `matmul_kernel` function, `matmul` afunction, and `ModelNew` class.
```python
import torch
import torch_npu
import torch.nn as nn
import triton
import triton.language as tl
import time
@triton.jit
def matmul_kernel(
a_ptr, b_ptr, c_ptr,
M, N, K,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr
):
"""Simplified matmul kernel without GROUP_SIZE_M optimization."""
# Each program computes one block in (M, N) grid
pid = tl.program_id(axis=0)
num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
pid_m = pid // num_pid_n
pid_n = pid % num_pid_n
# Compute offsets
offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
# Initialize accumulator
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
accumulator = tl.dot(a, b, accumulator)
a_ptrs += BLOCK_SIZE_K * stride_ak
b_ptrs += BLOCK_SIZE_K * stride_bk
c = accumulator.to(tl.float32)
# Write back with mask
offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
tl.store(c_ptrs, c, mask=c_mask)
def matmul(x, y):
M, K = x.shape
K, N = y.shape
output = torch.empty((M, N), device=x.device, dtype=torch.float32)
grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']), )
matmul_kernel[grid](
x, y, output, #
M, N, K, #
x.stride(0), x.stride(1), #
y.stride(0), y.stride(1), #
output.stride(0), output.stride(1), #
BLOCK_SIZE_M=16, BLOCK_SIZE_N=16, BLOCK_SIZE_K=8
)
return output
class ModelNew(nn.Module):
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
return matmul(x, y)
```
Now, you are given the following PyTorch architecture with name matrix_scalar_multiplication
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a matrix-scalar multiplication (C = A * s)
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, A: torch.Tensor, s: float) -> torch.Tensor:
"""
Performs matrix-scalar multiplication.
Args:
A: Input matrix of shape (M, N)
s: Scalar value
Returns:
C: Resulting matrix of shape (M, N)
"""
return A * s
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
"""
Applies Matrix Multiplication to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (M, K).
x (torch.Tensor): Input tensor of shape (K, N).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.matmul(x, y)
M, K, N = 64, 32, 128
def get_inputs():
x = torch.randn(M, K)
y = torch.randn(K, N)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name matmul**:
The transformtion includes three parts: `matmul_kernel` function, `matmul` afunction, and `ModelNew` class.
```python
import torch
import torch_npu
import torch.nn as nn
import triton
import triton.language as tl
import time
@triton.jit
def matmul_kernel(
a_ptr, b_ptr, c_ptr,
M, N, K,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr
):
"""Simplified matmul kernel without GROUP_SIZE_M optimization."""
# Each program computes one block in (M, N) grid
pid = tl.program_id(axis=0)
num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
pid_m = pid // num_pid_n
pid_n = pid % num_pid_n
# Compute offsets
offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
# Initialize accumulator
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
accumulator = tl.dot(a, b, accumulator)
a_ptrs += BLOCK_SIZE_K * stride_ak
b_ptrs += BLOCK_SIZE_K * stride_bk
c = accumulator.to(tl.float32)
# Write back with mask
offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
tl.store(c_ptrs, c, mask=c_mask)
def matmul(x, y):
M, K = x.shape
K, N = y.shape
output = torch.empty((M, N), device=x.device, dtype=torch.float32)
grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']), )
matmul_kernel[grid](
x, y, output, #
M, N, K, #
x.stride(0), x.stride(1), #
y.stride(0), y.stride(1), #
output.stride(0), output.stride(1), #
BLOCK_SIZE_M=16, BLOCK_SIZE_N=16, BLOCK_SIZE_K=8
)
return output
class ModelNew(nn.Module):
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
return matmul(x, y)
```
Now, you are given the following PyTorch architecture with name cumprod
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
A model that performs a cumulative product operation along a specified dimension.
Parameters:
dim (int): The dimension along which to perform the cumulative product operation.
"""
def __init__(self, dim):
"""
Initialize the CumulativeProductModel.
Args:
dim (int): The dimension along which to perform the cumulative product.
"""
super(Model, self).__init__()
self.dim = dim
def forward(self, x):
"""
Forward pass, computing the cumulative product along the specified dimension.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, *input_shape).
Returns:
torch.Tensor: Tensor of the same shape as `x` after applying cumulative product along `dim`.
"""
return torch.cumprod(x, dim=self.dim)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
"""
Applies Matrix Multiplication to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (M, K).
x (torch.Tensor): Input tensor of shape (K, N).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.matmul(x, y)
M, K, N = 64, 32, 128
def get_inputs():
x = torch.randn(M, K)
y = torch.randn(K, N)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name matmul**:
The transformtion includes three parts: `matmul_kernel` function, `matmul` afunction, and `ModelNew` class.
```python
import torch
import torch_npu
import torch.nn as nn
import triton
import triton.language as tl
import time
@triton.jit
def matmul_kernel(
a_ptr, b_ptr, c_ptr,
M, N, K,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr
):
"""Simplified matmul kernel without GROUP_SIZE_M optimization."""
# Each program computes one block in (M, N) grid
pid = tl.program_id(axis=0)
num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
pid_m = pid // num_pid_n
pid_n = pid % num_pid_n
# Compute offsets
offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
# Initialize accumulator
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
accumulator = tl.dot(a, b, accumulator)
a_ptrs += BLOCK_SIZE_K * stride_ak
b_ptrs += BLOCK_SIZE_K * stride_bk
c = accumulator.to(tl.float32)
# Write back with mask
offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
tl.store(c_ptrs, c, mask=c_mask)
def matmul(x, y):
M, K = x.shape
K, N = y.shape
output = torch.empty((M, N), device=x.device, dtype=torch.float32)
grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']), )
matmul_kernel[grid](
x, y, output, #
M, N, K, #
x.stride(0), x.stride(1), #
y.stride(0), y.stride(1), #
output.stride(0), output.stride(1), #
BLOCK_SIZE_M=16, BLOCK_SIZE_N=16, BLOCK_SIZE_K=8
)
return output
class ModelNew(nn.Module):
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
return matmul(x, y)
```
Now, you are given the following PyTorch architecture with name cumsum_reverse
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
A model that performs a reverse cumulative sum operation along a specified dimension.
Parameters:
dim (int): The dimension along which to perform the reverse cumulative sum.
"""
def __init__(self, dim):
super(Model, self).__init__()
self.dim = dim
def forward(self, x):
return torch.cumsum(x.flip(self.dim), dim=self.dim).flip(self.dim)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
"""
Applies Matrix Multiplication to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (M, K).
x (torch.Tensor): Input tensor of shape (K, N).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.matmul(x, y)
M, K, N = 64, 32, 128
def get_inputs():
x = torch.randn(M, K)
y = torch.randn(K, N)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name matmul**:
The transformtion includes three parts: `matmul_kernel` function, `matmul` afunction, and `ModelNew` class.
```python
import torch
import torch_npu
import torch.nn as nn
import triton
import triton.language as tl
import time
@triton.jit
def matmul_kernel(
a_ptr, b_ptr, c_ptr,
M, N, K,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr
):
"""Simplified matmul kernel without GROUP_SIZE_M optimization."""
# Each program computes one block in (M, N) grid
pid = tl.program_id(axis=0)
num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
pid_m = pid // num_pid_n
pid_n = pid % num_pid_n
# Compute offsets
offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
# Initialize accumulator
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
accumulator = tl.dot(a, b, accumulator)
a_ptrs += BLOCK_SIZE_K * stride_ak
b_ptrs += BLOCK_SIZE_K * stride_bk
c = accumulator.to(tl.float32)
# Write back with mask
offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
tl.store(c_ptrs, c, mask=c_mask)
def matmul(x, y):
M, K = x.shape
K, N = y.shape
output = torch.empty((M, N), device=x.device, dtype=torch.float32)
grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']), )
matmul_kernel[grid](
x, y, output, #
M, N, K, #
x.stride(0), x.stride(1), #
y.stride(0), y.stride(1), #
output.stride(0), output.stride(1), #
BLOCK_SIZE_M=16, BLOCK_SIZE_N=16, BLOCK_SIZE_K=8
)
return output
class ModelNew(nn.Module):
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
return matmul(x, y)
```
Now, you are given the following PyTorch architecture with name matmul_with_small_k_dimension
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a single matrix multiplication (C = A * B) with a small K dimension
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:
"""
Performs matrix multiplication.
Args:
A: Input tensor of shape (M, K).
B: Input tensor of shape (K, N).
Returns:
Output tensor of shape (M, N).
"""
return torch.matmul(A, B)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
"""
Applies Matrix Multiplication to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (M, K).
x (torch.Tensor): Input tensor of shape (K, N).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.matmul(x, y)
M, K, N = 64, 32, 128
def get_inputs():
x = torch.randn(M, K)
y = torch.randn(K, N)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name matmul**:
The transformtion includes three parts: `matmul_kernel` function, `matmul` afunction, and `ModelNew` class.
```python
import torch
import torch_npu
import torch.nn as nn
import triton
import triton.language as tl
import time
@triton.jit
def matmul_kernel(
a_ptr, b_ptr, c_ptr,
M, N, K,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr
):
"""Simplified matmul kernel without GROUP_SIZE_M optimization."""
# Each program computes one block in (M, N) grid
pid = tl.program_id(axis=0)
num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
pid_m = pid // num_pid_n
pid_n = pid % num_pid_n
# Compute offsets
offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
# Initialize accumulator
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
accumulator = tl.dot(a, b, accumulator)
a_ptrs += BLOCK_SIZE_K * stride_ak
b_ptrs += BLOCK_SIZE_K * stride_bk
c = accumulator.to(tl.float32)
# Write back with mask
offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
tl.store(c_ptrs, c, mask=c_mask)
def matmul(x, y):
M, K = x.shape
K, N = y.shape
output = torch.empty((M, N), device=x.device, dtype=torch.float32)
grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']), )
matmul_kernel[grid](
x, y, output, #
M, N, K, #
x.stride(0), x.stride(1), #
y.stride(0), y.stride(1), #
output.stride(0), output.stride(1), #
BLOCK_SIZE_M=16, BLOCK_SIZE_N=16, BLOCK_SIZE_K=8
)
return output
class ModelNew(nn.Module):
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
return matmul(x, y)
```
Now, you are given the following PyTorch architecture with name matmul_with_transposed_both
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a single matrix multiplication (C = A * B)
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:
"""
Performs matrix multiplication.
Args:
A: Input tensor of shape (M, K).
B: Input tensor of shape (K, N).
Returns:
Output tensor of shape (M, N).
"""
return torch.matmul(A.T, B.T)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 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
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
"""
Applies Matrix Multiplication to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (M, K).
x (torch.Tensor): Input tensor of shape (K, N).
Returns:
torch.Tensor: Output tensor with Softmax applied, same shape as input.
"""
return torch.matmul(x, y)
M, K, N = 64, 32, 128
def get_inputs():
x = torch.randn(M, K)
y = torch.randn(K, N)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name matmul**:
The transformtion includes three parts: `matmul_kernel` function, `matmul` afunction, and `ModelNew` class.
```python
import torch
import torch_npu
import torch.nn as nn
import triton
import triton.language as tl
import time
@triton.jit
def matmul_kernel(
a_ptr, b_ptr, c_ptr,
M, N, K,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr
):
"""Simplified matmul kernel without GROUP_SIZE_M optimization."""
# Each program computes one block in (M, N) grid
pid = tl.program_id(axis=0)
num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
pid_m = pid // num_pid_n
pid_n = pid % num_pid_n
# Compute offsets
offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
# Initialize accumulator
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
accumulator = tl.dot(a, b, accumulator)
a_ptrs += BLOCK_SIZE_K * stride_ak
b_ptrs += BLOCK_SIZE_K * stride_bk
c = accumulator.to(tl.float32)
# Write back with mask
offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
tl.store(c_ptrs, c, mask=c_mask)
def matmul(x, y):
M, K = x.shape
K, N = y.shape
output = torch.empty((M, N), device=x.device, dtype=torch.float32)
grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']), )
matmul_kernel[grid](
x, y, output, #
M, N, K, #
x.stride(0), x.stride(1), #
y.stride(0), y.stride(1), #
output.stride(0), output.stride(1), #
BLOCK_SIZE_M=16, BLOCK_SIZE_N=16, BLOCK_SIZE_K=8
)
return output
class ModelNew(nn.Module):
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
return matmul(x, y)
```
Now, you are given the following PyTorch architecture with name matrix_vector_multiplication
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs matrix-vector multiplication (C = A * B).
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:
"""
Performs matrix-vector multiplication.
Args:
A: Input matrix of shape (M, K).
B: Input vector of shape (K, 1).
Returns:
Output vector of shape (M, 1).
"""
return torch.matmul(A, B)
```
Your task: Replace relevant PyTorch operators in the architecture named Model with custom AscendC Triton kernels. Generate an optimized version named ModelNew, including the two functions and ModelNew class listed above. Just output the code, no other text, and NO testing code! The output code must be enclosed within ``` delimiters.
| 0.002103 |
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