id
stringlengths 3
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stringlengths 4.64k
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| ref_time
float64 0
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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 |
four_dim_tensor_matrix_multiplication
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python
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 four_dim_tensor_matrix_multiplication
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Performs 4D tensor-matrix multiplication:
C[b, i, j, k] = sum_l A[b, i, j, l] * B[l, k]
Args:
A (torch.Tensor): Input 4D tensor of shape (b, i, j, l)
B (torch.Tensor): Input matrix of shape (l, k)
Returns:
torch.Tensor: Output 4D tensor of shape (b, i, j, k)
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, A, B):
"""
Performs the 4D tensor-matrix multiplication.
Args:
A (torch.Tensor): Input 4D tensor of shape (b, i, j, l)
B (torch.Tensor): Input matrix of shape (l, k)
Returns:
torch.Tensor: Output 4D tensor of shape (b, i, j, k)
"""
return torch.einsum("bijl,lk->bijk", 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.
| 5.236348 |
matmul_with_transposed_b
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python
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_b
```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, 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.
| 0.278551 |
matmul_with_large_k_dimension
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python
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_large_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 large K dimension
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:
"""
Performs matrix multiplication of A and B.
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.
| 0.065918 |
matmul_for_lower_triangular_matrices
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python
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_for_lower_triangular_matrices
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a matrix multiplication (C = A * B) where A and B are lower triangular matrices.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, A, B):
"""
Performs matrix multiplication of lower triangular matrices A and B.
Args:
A (torch.Tensor): Lower triangular matrix of shape (N, N).
B (torch.Tensor): Lower triangular matrix of shape (N, N).
Returns:
torch.Tensor: The result of matrix multiplication C of shape (N, N).
"""
return torch.tril(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.
| 0.505047 |
matmul_with_irregular_shapes
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python
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_irregular_shapes
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a single matrix multiplication (C = A * B) with irregular shapes
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:
"""
Performs matrix multiplication of A and B.
Args:
A: Input tensor with shape (M, K).
B: Input tensor with shape (K, N).
Returns:
C: Output tensor with 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.
| 1.171759 |
batched_matrix_multiplication
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python
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 batched_matrix_multiplication
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Performs batched matrix multiplication (C = A * B) where A, B, and C have the same batch dimension.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:
"""
Performs batched matrix multiplication.
Args:
A: Input tensor of shape (batch_size, m, k).
B: Input tensor of shape (batch_size, k, n).
Returns:
C: Output tensor of shape (batch_size, m, n).
"""
return torch.bmm(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.
| 0.319429 |
matmul_for_symmetric_matrices
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python
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_for_symmetric_matrices
```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 and B being symmetric matrices.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, A, B):
"""
Performs matrix multiplication of two symmetric matrices.
Args:
A (torch.Tensor): Input matrix A, shape (N, N), symmetric.
B (torch.Tensor): Input matrix B, shape (N, N), symmetric.
Returns:
torch.Tensor: Output matrix C, shape (N, 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.
| 0.645905 |
standard_matrix_multiplication
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python
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 standard_matrix_multiplication
```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, 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.
| 0.088071 |
square_matrix_multiplication
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python
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 square_matrix_multiplication
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a single square matrix multiplication (C = A * B)
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:
"""
Performs the matrix multiplication.
Args:
A (torch.Tensor): Input matrix A of shape (N, N).
B (torch.Tensor): Input matrix B of shape (N, N).
Returns:
torch.Tensor: Output matrix C of shape (N, 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.
| 0.082542 |
tall_skinny_matrix_multiplication
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python
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 tall_skinny_matrix_multiplication
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a single matrix multiplication (C = A * B) where one of the matrices is tall and skinny (M >> N or N >> M)
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, A, B):
"""
Performs the matrix multiplication.
Args:
A (torch.Tensor): Input matrix of shape (M, K) or (K, M) where M >> N or N >> M.
B (torch.Tensor): Input matrix of shape (K, N) or (N, K) where M >> N or N >> M.
Returns:
torch.Tensor: Output matrix of shape (M, N) or (N, M)
"""
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.
| 0.421983 |
matmul_for_upper_triangular_matrices
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python
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_for_upper_triangular_matrices
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs matrix multiplication (C = A * B) for upper triangular matrices.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, A, B):
"""
Performs matrix multiplication for upper triangular matrices.
Args:
A (torch.Tensor): Upper triangular matrix of shape (N, N).
B (torch.Tensor): Upper triangular matrix of shape (N, N).
Returns:
torch.Tensor: The product of A and B, also an upper triangular matrix of shape (N, N).
"""
return torch.triu(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.
| 0.582953 |
matmul_with_diagonal_matrices
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python
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_diagonal_matrices
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a matrix multiplication of a diagonal matrix with another matrix.
C = diag(A) * B
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, A, B):
"""
Performs the matrix multiplication.
Args:
A (torch.Tensor): A 1D tensor representing the diagonal of the diagonal matrix. Shape: (N,).
B (torch.Tensor): A 2D tensor representing the second matrix. Shape: (N, M).
Returns:
torch.Tensor: The result of the matrix multiplication. Shape: (N, M).
"""
return torch.diag(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.
| 0.582444 |
three_dim_tensor_matrix_multiplication
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name matmul**:
```python
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 three_dim_tensor_matrix_multiplication
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Performs 3D tensor-matrix multiplication.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, A, B):
"""
Performs 3D tensor-matrix multiplication.
Args:
A (torch.Tensor): Input 3D tensor of shape (N, M, K).
B (torch.Tensor): Input matrix of shape (K, L).
Returns:
torch.Tensor: Output tensor of shape (N, M, L), resulting from the multiplication of A and B along the last dimension of A.
"""
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.
| 0.186669 |
instance_norm
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Input shape from an expected input of size
(*, normalized_shape). It defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): A value added to the denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return nn.functional.layer_norm(x, normalized_shape, weight, bias, eps)
batch_size = 16
features = 64
dim1 = 256
dim2 = 256
def get_inputs():
x = torch.randn(batch_size, features, dim1, dim2)
normalized_shape = (features, dim1, dim2)
weight = torch.ones(normalized_shape)
bias = torch.zeros(normalized_shape)
eps = 1e-5
return [x, normalized_shape, weight, bias, eps]
def get_init_inputs():
return []
```
**Transformed Triton Architecture with name layer_norm**:
The transformtion includes three parts: `layer_norm_kernel` function, `layer_norm` 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 _layer_norm_kernel(
X, # Input pointer
Y, # Output pointer
W, # Weight pointer
B, # Bias pointer
Mean, # Mean pointer
Rstd, # 1/std pointer
stride, # How much to move the pointer per row
N, # Number of columns in X
eps, # Epsilon to avoid division by zero
BLOCK_SIZE: tl.constexpr,
):
# Map program id to the corresponding row of X and Y
row = tl.program_id(0)
Y += row * stride
X += row * stride
# Compute mean
mean = 0
_mean = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
a = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
_mean += a
mean = tl.sum(_mean, axis=0) / N
# Compute variance
_var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
x = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
x = tl.where(cols < N, x - mean, 0.)
_var += x * x
var = tl.sum(_var, axis=0) / N
rstd = 1 / tl.sqrt(var + eps)
# Store mean and rstd
tl.store(Mean + row, mean)
tl.store(Rstd + row, rstd)
# Normalize and apply linear transformation
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
mask = cols < N
w = tl.load(W + cols, mask=mask)
b = tl.load(B + cols, mask=mask)
x = tl.load(X + cols, mask=mask, other=0.).to(tl.float32)
x_hat = (x - mean) * rstd
y = x_hat * w + b
# Write output
tl.store(Y + cols, y, mask=mask)
@torch.inference_mode()
def layer_norm(x, normalized_shape, weight, bias, eps=1e-5):
# Allocate output tensor with the same shape and dtype as input
y = torch.empty_like(x)
# Reshape input x to 2D shape [-1, feature_dim] to normalize the last dimension
x_arg = x.reshape(-1, x.shape[-1])
M, N = x_arg.shape
mean = torch.empty((M, ), dtype=torch.float32, device=x.device)
rstd = torch.empty((M, ), dtype=torch.float32, device=x.device)
BLOCK_SIZE = 1024
# Launch kernel
kernel = _layer_norm_kernel[(M, )]( # M is the number of blocks, launch grid=(M,)
x_arg, y, weight, bias, mean, rstd, # Inputs, outputs, and intermediates
x_arg.stride(0), N, eps,
BLOCK_SIZE=BLOCK_SIZE)
# Return normalized output
return y
class ModelNew(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Expected input shape (*, normalized_shape). Defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): Value added to denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return layer_norm(x, normalized_shape, weight, bias, eps)
```
Now, you are given the following PyTorch architecture with name instance_norm
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs Instance Normalization.
"""
def __init__(self, num_features: int):
"""
Initializes the InstanceNorm layer.
Args:
num_features (int): Number of features in the input tensor.
"""
super(Model, self).__init__()
self.inorm = nn.InstanceNorm2d(num_features=num_features)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Instance Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features, height, width).
Returns:
torch.Tensor: Output tensor with Instance Normalization applied, same shape as input.
"""
return self.inorm(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.
| 0.110181 |
l2_norm
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Input shape from an expected input of size
(*, normalized_shape). It defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): A value added to the denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return nn.functional.layer_norm(x, normalized_shape, weight, bias, eps)
batch_size = 16
features = 64
dim1 = 256
dim2 = 256
def get_inputs():
x = torch.randn(batch_size, features, dim1, dim2)
normalized_shape = (features, dim1, dim2)
weight = torch.ones(normalized_shape)
bias = torch.zeros(normalized_shape)
eps = 1e-5
return [x, normalized_shape, weight, bias, eps]
def get_init_inputs():
return []
```
**Transformed Triton Architecture with name layer_norm**:
The transformtion includes three parts: `layer_norm_kernel` function, `layer_norm` 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 _layer_norm_kernel(
X, # Input pointer
Y, # Output pointer
W, # Weight pointer
B, # Bias pointer
Mean, # Mean pointer
Rstd, # 1/std pointer
stride, # How much to move the pointer per row
N, # Number of columns in X
eps, # Epsilon to avoid division by zero
BLOCK_SIZE: tl.constexpr,
):
# Map program id to the corresponding row of X and Y
row = tl.program_id(0)
Y += row * stride
X += row * stride
# Compute mean
mean = 0
_mean = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
a = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
_mean += a
mean = tl.sum(_mean, axis=0) / N
# Compute variance
_var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
x = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
x = tl.where(cols < N, x - mean, 0.)
_var += x * x
var = tl.sum(_var, axis=0) / N
rstd = 1 / tl.sqrt(var + eps)
# Store mean and rstd
tl.store(Mean + row, mean)
tl.store(Rstd + row, rstd)
# Normalize and apply linear transformation
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
mask = cols < N
w = tl.load(W + cols, mask=mask)
b = tl.load(B + cols, mask=mask)
x = tl.load(X + cols, mask=mask, other=0.).to(tl.float32)
x_hat = (x - mean) * rstd
y = x_hat * w + b
# Write output
tl.store(Y + cols, y, mask=mask)
@torch.inference_mode()
def layer_norm(x, normalized_shape, weight, bias, eps=1e-5):
# Allocate output tensor with the same shape and dtype as input
y = torch.empty_like(x)
# Reshape input x to 2D shape [-1, feature_dim] to normalize the last dimension
x_arg = x.reshape(-1, x.shape[-1])
M, N = x_arg.shape
mean = torch.empty((M, ), dtype=torch.float32, device=x.device)
rstd = torch.empty((M, ), dtype=torch.float32, device=x.device)
BLOCK_SIZE = 1024
# Launch kernel
kernel = _layer_norm_kernel[(M, )]( # M is the number of blocks, launch grid=(M,)
x_arg, y, weight, bias, mean, rstd, # Inputs, outputs, and intermediates
x_arg.stride(0), N, eps,
BLOCK_SIZE=BLOCK_SIZE)
# Return normalized output
return y
class ModelNew(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Expected input shape (*, normalized_shape). Defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): Value added to denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return layer_norm(x, normalized_shape, weight, bias, eps)
```
Now, you are given the following PyTorch architecture with name l2_norm
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs L2 normalization.
"""
def __init__(self):
"""
Initializes the L2Norm layer.
Args:
dim (int): Dimension along which to normalize.
"""
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies L2 normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, dim, *).
Returns:
torch.Tensor: Output tensor with L2 normalization applied, same shape as input.
"""
return x / torch.norm(x, p=2, dim=1, keepdim=True)
```
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.
| 0.010375 |
rms_norm
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Input shape from an expected input of size
(*, normalized_shape). It defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): A value added to the denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return nn.functional.layer_norm(x, normalized_shape, weight, bias, eps)
batch_size = 16
features = 64
dim1 = 256
dim2 = 256
def get_inputs():
x = torch.randn(batch_size, features, dim1, dim2)
normalized_shape = (features, dim1, dim2)
weight = torch.ones(normalized_shape)
bias = torch.zeros(normalized_shape)
eps = 1e-5
return [x, normalized_shape, weight, bias, eps]
def get_init_inputs():
return []
```
**Transformed Triton Architecture with name layer_norm**:
The transformtion includes three parts: `layer_norm_kernel` function, `layer_norm` 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 _layer_norm_kernel(
X, # Input pointer
Y, # Output pointer
W, # Weight pointer
B, # Bias pointer
Mean, # Mean pointer
Rstd, # 1/std pointer
stride, # How much to move the pointer per row
N, # Number of columns in X
eps, # Epsilon to avoid division by zero
BLOCK_SIZE: tl.constexpr,
):
# Map program id to the corresponding row of X and Y
row = tl.program_id(0)
Y += row * stride
X += row * stride
# Compute mean
mean = 0
_mean = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
a = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
_mean += a
mean = tl.sum(_mean, axis=0) / N
# Compute variance
_var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
x = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
x = tl.where(cols < N, x - mean, 0.)
_var += x * x
var = tl.sum(_var, axis=0) / N
rstd = 1 / tl.sqrt(var + eps)
# Store mean and rstd
tl.store(Mean + row, mean)
tl.store(Rstd + row, rstd)
# Normalize and apply linear transformation
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
mask = cols < N
w = tl.load(W + cols, mask=mask)
b = tl.load(B + cols, mask=mask)
x = tl.load(X + cols, mask=mask, other=0.).to(tl.float32)
x_hat = (x - mean) * rstd
y = x_hat * w + b
# Write output
tl.store(Y + cols, y, mask=mask)
@torch.inference_mode()
def layer_norm(x, normalized_shape, weight, bias, eps=1e-5):
# Allocate output tensor with the same shape and dtype as input
y = torch.empty_like(x)
# Reshape input x to 2D shape [-1, feature_dim] to normalize the last dimension
x_arg = x.reshape(-1, x.shape[-1])
M, N = x_arg.shape
mean = torch.empty((M, ), dtype=torch.float32, device=x.device)
rstd = torch.empty((M, ), dtype=torch.float32, device=x.device)
BLOCK_SIZE = 1024
# Launch kernel
kernel = _layer_norm_kernel[(M, )]( # M is the number of blocks, launch grid=(M,)
x_arg, y, weight, bias, mean, rstd, # Inputs, outputs, and intermediates
x_arg.stride(0), N, eps,
BLOCK_SIZE=BLOCK_SIZE)
# Return normalized output
return y
class ModelNew(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Expected input shape (*, normalized_shape). Defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): Value added to denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return layer_norm(x, normalized_shape, weight, bias, eps)
```
Now, you are given the following PyTorch architecture with name rms_norm
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs RMS Normalization.
"""
def __init__(self, num_features: int, eps: float = 1e-5):
"""
Initializes the RMSNorm layer.
Args:
num_features (int): Number of features in the input tensor.
eps (float, optional): A small value added to the denominator to avoid division by zero. Defaults to 1e-5.
"""
super(Model, self).__init__()
self.num_features = num_features
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies RMS Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features, *).
Returns:
torch.Tensor: Output tensor with RMS Normalization applied, same shape as input.
"""
# Calculate the RMS along the feature dimension
rms = torch.sqrt(torch.mean(x ** 2, dim=1, keepdim=True) + self.eps)
# Normalize the input by dividing by the RMS
return x / rms
```
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.
| 0.205776 |
layer_norm
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Input shape from an expected input of size
(*, normalized_shape). It defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): A value added to the denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return nn.functional.layer_norm(x, normalized_shape, weight, bias, eps)
batch_size = 16
features = 64
dim1 = 256
dim2 = 256
def get_inputs():
x = torch.randn(batch_size, features, dim1, dim2)
normalized_shape = (features, dim1, dim2)
weight = torch.ones(normalized_shape)
bias = torch.zeros(normalized_shape)
eps = 1e-5
return [x, normalized_shape, weight, bias, eps]
def get_init_inputs():
return []
```
**Transformed Triton Architecture with name layer_norm**:
The transformtion includes three parts: `layer_norm_kernel` function, `layer_norm` 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 _layer_norm_kernel(
X, # Input pointer
Y, # Output pointer
W, # Weight pointer
B, # Bias pointer
Mean, # Mean pointer
Rstd, # 1/std pointer
stride, # How much to move the pointer per row
N, # Number of columns in X
eps, # Epsilon to avoid division by zero
BLOCK_SIZE: tl.constexpr,
):
# Map program id to the corresponding row of X and Y
row = tl.program_id(0)
Y += row * stride
X += row * stride
# Compute mean
mean = 0
_mean = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
a = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
_mean += a
mean = tl.sum(_mean, axis=0) / N
# Compute variance
_var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
x = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
x = tl.where(cols < N, x - mean, 0.)
_var += x * x
var = tl.sum(_var, axis=0) / N
rstd = 1 / tl.sqrt(var + eps)
# Store mean and rstd
tl.store(Mean + row, mean)
tl.store(Rstd + row, rstd)
# Normalize and apply linear transformation
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
mask = cols < N
w = tl.load(W + cols, mask=mask)
b = tl.load(B + cols, mask=mask)
x = tl.load(X + cols, mask=mask, other=0.).to(tl.float32)
x_hat = (x - mean) * rstd
y = x_hat * w + b
# Write output
tl.store(Y + cols, y, mask=mask)
@torch.inference_mode()
def layer_norm(x, normalized_shape, weight, bias, eps=1e-5):
# Allocate output tensor with the same shape and dtype as input
y = torch.empty_like(x)
# Reshape input x to 2D shape [-1, feature_dim] to normalize the last dimension
x_arg = x.reshape(-1, x.shape[-1])
M, N = x_arg.shape
mean = torch.empty((M, ), dtype=torch.float32, device=x.device)
rstd = torch.empty((M, ), dtype=torch.float32, device=x.device)
BLOCK_SIZE = 1024
# Launch kernel
kernel = _layer_norm_kernel[(M, )]( # M is the number of blocks, launch grid=(M,)
x_arg, y, weight, bias, mean, rstd, # Inputs, outputs, and intermediates
x_arg.stride(0), N, eps,
BLOCK_SIZE=BLOCK_SIZE)
# Return normalized output
return y
class ModelNew(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Expected input shape (*, normalized_shape). Defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): Value added to denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return layer_norm(x, normalized_shape, weight, bias, eps)
```
Now, you are given the following PyTorch architecture with name layer_norm
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Input shape from an expected input of size
(*, normalized_shape). It defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): A value added to the denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return nn.functional.layer_norm(x, normalized_shape, weight, bias, eps)
```
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.
| 0.113798 |
batch_norm
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Input shape from an expected input of size
(*, normalized_shape). It defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): A value added to the denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return nn.functional.layer_norm(x, normalized_shape, weight, bias, eps)
batch_size = 16
features = 64
dim1 = 256
dim2 = 256
def get_inputs():
x = torch.randn(batch_size, features, dim1, dim2)
normalized_shape = (features, dim1, dim2)
weight = torch.ones(normalized_shape)
bias = torch.zeros(normalized_shape)
eps = 1e-5
return [x, normalized_shape, weight, bias, eps]
def get_init_inputs():
return []
```
**Transformed Triton Architecture with name layer_norm**:
The transformtion includes three parts: `layer_norm_kernel` function, `layer_norm` 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 _layer_norm_kernel(
X, # Input pointer
Y, # Output pointer
W, # Weight pointer
B, # Bias pointer
Mean, # Mean pointer
Rstd, # 1/std pointer
stride, # How much to move the pointer per row
N, # Number of columns in X
eps, # Epsilon to avoid division by zero
BLOCK_SIZE: tl.constexpr,
):
# Map program id to the corresponding row of X and Y
row = tl.program_id(0)
Y += row * stride
X += row * stride
# Compute mean
mean = 0
_mean = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
a = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
_mean += a
mean = tl.sum(_mean, axis=0) / N
# Compute variance
_var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
x = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
x = tl.where(cols < N, x - mean, 0.)
_var += x * x
var = tl.sum(_var, axis=0) / N
rstd = 1 / tl.sqrt(var + eps)
# Store mean and rstd
tl.store(Mean + row, mean)
tl.store(Rstd + row, rstd)
# Normalize and apply linear transformation
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
mask = cols < N
w = tl.load(W + cols, mask=mask)
b = tl.load(B + cols, mask=mask)
x = tl.load(X + cols, mask=mask, other=0.).to(tl.float32)
x_hat = (x - mean) * rstd
y = x_hat * w + b
# Write output
tl.store(Y + cols, y, mask=mask)
@torch.inference_mode()
def layer_norm(x, normalized_shape, weight, bias, eps=1e-5):
# Allocate output tensor with the same shape and dtype as input
y = torch.empty_like(x)
# Reshape input x to 2D shape [-1, feature_dim] to normalize the last dimension
x_arg = x.reshape(-1, x.shape[-1])
M, N = x_arg.shape
mean = torch.empty((M, ), dtype=torch.float32, device=x.device)
rstd = torch.empty((M, ), dtype=torch.float32, device=x.device)
BLOCK_SIZE = 1024
# Launch kernel
kernel = _layer_norm_kernel[(M, )]( # M is the number of blocks, launch grid=(M,)
x_arg, y, weight, bias, mean, rstd, # Inputs, outputs, and intermediates
x_arg.stride(0), N, eps,
BLOCK_SIZE=BLOCK_SIZE)
# Return normalized output
return y
class ModelNew(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Expected input shape (*, normalized_shape). Defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): Value added to denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return layer_norm(x, normalized_shape, weight, bias, eps)
```
Now, you are given the following PyTorch architecture with name batch_norm
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs Batch Normalization.
"""
def __init__(self, num_features: int):
"""
Initializes the BatchNorm layer.
Args:
num_features (int): Number of features in the input tensor.
"""
super(Model, self).__init__()
self.bn = nn.BatchNorm2d(num_features=num_features)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Batch Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features, *).
Returns:
torch.Tensor: Output tensor with Batch Normalization applied, same shape as input.
"""
return self.bn(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.
| 0.115461 |
frobenius_norm
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Input shape from an expected input of size
(*, normalized_shape). It defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): A value added to the denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return nn.functional.layer_norm(x, normalized_shape, weight, bias, eps)
batch_size = 16
features = 64
dim1 = 256
dim2 = 256
def get_inputs():
x = torch.randn(batch_size, features, dim1, dim2)
normalized_shape = (features, dim1, dim2)
weight = torch.ones(normalized_shape)
bias = torch.zeros(normalized_shape)
eps = 1e-5
return [x, normalized_shape, weight, bias, eps]
def get_init_inputs():
return []
```
**Transformed Triton Architecture with name layer_norm**:
The transformtion includes three parts: `layer_norm_kernel` function, `layer_norm` 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 _layer_norm_kernel(
X, # Input pointer
Y, # Output pointer
W, # Weight pointer
B, # Bias pointer
Mean, # Mean pointer
Rstd, # 1/std pointer
stride, # How much to move the pointer per row
N, # Number of columns in X
eps, # Epsilon to avoid division by zero
BLOCK_SIZE: tl.constexpr,
):
# Map program id to the corresponding row of X and Y
row = tl.program_id(0)
Y += row * stride
X += row * stride
# Compute mean
mean = 0
_mean = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
a = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
_mean += a
mean = tl.sum(_mean, axis=0) / N
# Compute variance
_var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
x = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
x = tl.where(cols < N, x - mean, 0.)
_var += x * x
var = tl.sum(_var, axis=0) / N
rstd = 1 / tl.sqrt(var + eps)
# Store mean and rstd
tl.store(Mean + row, mean)
tl.store(Rstd + row, rstd)
# Normalize and apply linear transformation
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
mask = cols < N
w = tl.load(W + cols, mask=mask)
b = tl.load(B + cols, mask=mask)
x = tl.load(X + cols, mask=mask, other=0.).to(tl.float32)
x_hat = (x - mean) * rstd
y = x_hat * w + b
# Write output
tl.store(Y + cols, y, mask=mask)
@torch.inference_mode()
def layer_norm(x, normalized_shape, weight, bias, eps=1e-5):
# Allocate output tensor with the same shape and dtype as input
y = torch.empty_like(x)
# Reshape input x to 2D shape [-1, feature_dim] to normalize the last dimension
x_arg = x.reshape(-1, x.shape[-1])
M, N = x_arg.shape
mean = torch.empty((M, ), dtype=torch.float32, device=x.device)
rstd = torch.empty((M, ), dtype=torch.float32, device=x.device)
BLOCK_SIZE = 1024
# Launch kernel
kernel = _layer_norm_kernel[(M, )]( # M is the number of blocks, launch grid=(M,)
x_arg, y, weight, bias, mean, rstd, # Inputs, outputs, and intermediates
x_arg.stride(0), N, eps,
BLOCK_SIZE=BLOCK_SIZE)
# Return normalized output
return y
class ModelNew(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Expected input shape (*, normalized_shape). Defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): Value added to denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return layer_norm(x, normalized_shape, weight, bias, eps)
```
Now, you are given the following PyTorch architecture with name frobenius_norm
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs Frobenius norm normalization.
"""
def __init__(self):
"""
Initializes the Frobenius norm normalization layer.
"""
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Frobenius norm normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of arbitrary shape.
Returns:
torch.Tensor: Output tensor with Frobenius norm normalization applied, same shape as input.
"""
norm = torch.norm(x, p='fro')
return x / norm
```
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.
| 0.164778 |
group_norm
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Input shape from an expected input of size
(*, normalized_shape). It defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): A value added to the denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return nn.functional.layer_norm(x, normalized_shape, weight, bias, eps)
batch_size = 16
features = 64
dim1 = 256
dim2 = 256
def get_inputs():
x = torch.randn(batch_size, features, dim1, dim2)
normalized_shape = (features, dim1, dim2)
weight = torch.ones(normalized_shape)
bias = torch.zeros(normalized_shape)
eps = 1e-5
return [x, normalized_shape, weight, bias, eps]
def get_init_inputs():
return []
```
**Transformed Triton Architecture with name layer_norm**:
The transformtion includes three parts: `layer_norm_kernel` function, `layer_norm` 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 _layer_norm_kernel(
X, # Input pointer
Y, # Output pointer
W, # Weight pointer
B, # Bias pointer
Mean, # Mean pointer
Rstd, # 1/std pointer
stride, # How much to move the pointer per row
N, # Number of columns in X
eps, # Epsilon to avoid division by zero
BLOCK_SIZE: tl.constexpr,
):
# Map program id to the corresponding row of X and Y
row = tl.program_id(0)
Y += row * stride
X += row * stride
# Compute mean
mean = 0
_mean = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
a = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
_mean += a
mean = tl.sum(_mean, axis=0) / N
# Compute variance
_var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
x = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
x = tl.where(cols < N, x - mean, 0.)
_var += x * x
var = tl.sum(_var, axis=0) / N
rstd = 1 / tl.sqrt(var + eps)
# Store mean and rstd
tl.store(Mean + row, mean)
tl.store(Rstd + row, rstd)
# Normalize and apply linear transformation
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
mask = cols < N
w = tl.load(W + cols, mask=mask)
b = tl.load(B + cols, mask=mask)
x = tl.load(X + cols, mask=mask, other=0.).to(tl.float32)
x_hat = (x - mean) * rstd
y = x_hat * w + b
# Write output
tl.store(Y + cols, y, mask=mask)
@torch.inference_mode()
def layer_norm(x, normalized_shape, weight, bias, eps=1e-5):
# Allocate output tensor with the same shape and dtype as input
y = torch.empty_like(x)
# Reshape input x to 2D shape [-1, feature_dim] to normalize the last dimension
x_arg = x.reshape(-1, x.shape[-1])
M, N = x_arg.shape
mean = torch.empty((M, ), dtype=torch.float32, device=x.device)
rstd = torch.empty((M, ), dtype=torch.float32, device=x.device)
BLOCK_SIZE = 1024
# Launch kernel
kernel = _layer_norm_kernel[(M, )]( # M is the number of blocks, launch grid=(M,)
x_arg, y, weight, bias, mean, rstd, # Inputs, outputs, and intermediates
x_arg.stride(0), N, eps,
BLOCK_SIZE=BLOCK_SIZE)
# Return normalized output
return y
class ModelNew(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Expected input shape (*, normalized_shape). Defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): Value added to denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return layer_norm(x, normalized_shape, weight, bias, eps)
```
Now, you are given the following PyTorch architecture with name group_norm
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs Group Normalization.
"""
def __init__(self, num_features: int, num_groups: int):
"""
Initializes the GroupNorm layer.
Args:
num_features (int): Number of features in the input tensor.
num_groups (int): Number of groups to divide the channels into.
"""
super(Model, self).__init__()
self.gn = nn.GroupNorm(num_groups=num_groups, num_channels=num_features)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Group Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features, *).
Returns:
torch.Tensor: Output tensor with Group Normalization applied, same shape as input.
"""
return self.gn(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.
| 0.105444 |
adam
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```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 adam
```python
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, beta1=0.9, beta2=0.999, lr=1e-3, eps=1e-8, step=1):
super().__init__()
self.beta1 = beta1
self.beta2 = beta2
self.lr = lr
self.eps = eps
self.step = step
def forward(self, param, grad, m, v):
m = self.beta1 * m + (1 - self.beta1) * grad
v = self.beta2 * v + (1 - self.beta2) * grad.pow(2)
m_hat = m / (1 - self.beta1 ** self.step)
v_hat = v / (1 - self.beta2 ** self.step)
param = param - self.lr * m_hat / (v_hat.sqrt() + self.eps)
return param
```
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.
| 0.021668 |
adagrad
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```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 adagrad
```python
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, lr=1e-2, eps=1e-10):
super().__init__()
self.lr = lr
self.eps = eps
def forward(self, param, grad, accum):
accum = accum + grad.pow(2)
param = param - self.lr * grad / (accum.sqrt() + self.eps)
return param
```
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.
| 0.009261 |
sgd
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```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 sgd
```python
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, momentum=0.9, lr=1e-2):
super().__init__()
self.momentum = momentum
self.lr = lr
def forward(self, param, grad, velocity):
velocity = self.momentum * velocity + grad
param = param - self.lr * velocity
return param
```
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.
| 0.005109 |
rmsprop
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name softmax**:
```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 rmsprop
```python
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, lr=1e-3, alpha=0.99, eps=1e-8):
super().__init__()
self.lr = lr
self.alpha = alpha
self.eps = eps
def forward(self, param, grad, v):
v = self.alpha * v + (1 - self.alpha) * grad.pow(2)
param = param - self.lr * grad / (v.sqrt() + self.eps)
return param
```
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.
| 0.013808 |
max_pooling_3d
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return torch.nn.functional.conv2d(x.unsqueeze(0), weight, stride=1).squeeze(0)
C_in, C_out, H, W, KH, KW = 3, 8, 32, 32, 3, 3
def get_inputs():
x = torch.randn(C_in, H, W)
y = torch.randn(C_out, C_in, KH, KW)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name conv2d**:
The transformtion includes three parts: `conv2d_kernel` function, `conv2d` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
import time
# -----------------------------
# Triton 2D Convolution Kernel
# -----------------------------
@triton.jit
def conv2d_kernel(
output_ptr, input_ptr, weight_ptr,
H: tl.constexpr, W: tl.constexpr, # Input height and width
KH: tl.constexpr, KW: tl.constexpr, # Kernel height and width
IC: tl.constexpr, OC: tl.constexpr, # Input/output channels
stride_h: tl.constexpr, stride_w: tl.constexpr,
BLOCK_H: tl.constexpr, BLOCK_W: tl.constexpr
):
pid_h = tl.program_id(0)
pid_w = tl.program_id(1)
for oc in range(OC):
for oh in range(BLOCK_H):
for ow in range(BLOCK_W):
h = pid_h * BLOCK_H + oh
w = pid_w * BLOCK_W + ow
acc = 0.0
if h < H - KH + 1 and w < W - KW + 1:
for ic in range(IC):
for kh in range(KH):
for kw in range(KW):
x = tl.load(input_ptr + ic*H*W + (h+kh)*W + (w+kw))
k = tl.load(weight_ptr + oc*IC*KH*KW + ic*KH*KW + kh*KW + kw)
acc += x * k
tl.store(output_ptr + oc*(H-KH+1)*(W-KW+1) + h*(W-KW+1) + w, acc)
# -----------------------------
# Python wrapper
# -----------------------------
def conv2d(x, weight, stride=(1,1), block_size=(4,4)):
"""
x: (C_in, H, W)
weight: (C_out, C_in, KH, KW)
"""
C_in, H, W = x.shape
C_out, _, KH, KW = weight.shape
SH, SW = stride
out_H = (H - KH) // SH + 1
out_W = (W - KW) // SW + 1
y = torch.empty((C_out, out_H, out_W), device=x.device, dtype=x.dtype)
grid = (triton.cdiv(out_H, block_size[0]), triton.cdiv(out_W, block_size[1]))
conv2d_kernel[grid](
y, x, weight,
H, W, KH, KW, C_in, C_out,
SH, SW,
block_size[0], block_size[1]
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
stride: tuple = (1,1),
block_size: tuple = (2,2)
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
stride (tuple): Stride for the convolution (stride_h, stride_w).
block_size (tuple): Block size for Triton kernel (BLOCK_H, BLOCK_W).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return conv2d(x, weight, stride, block_size)
```
Now, you are given the following PyTorch architecture with name max_pooling_3d
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs Max Pooling 3D.
"""
def __init__(self, kernel_size: int, stride: int = None, padding: int = 0, dilation: int = 1, return_indices: bool = False, ceil_mode: bool = False):
"""
Initializes the Max Pooling 3D layer.
Args:
kernel_size (int): Size of the kernel for the max pooling operation.
stride (int, optional): Stride of the pooling operation. Defaults to None, which means stride is equal to kernel_size.
padding (int, optional): Padding applied to the input tensor. Defaults to 0.
dilation (int, optional): Spacing between kernel elements. Defaults to 1.
return_indices (bool, optional): Whether to return indices of the maximum values. Defaults to False.
ceil_mode (bool, optional): When True, the output size is ceil(input_size / stride) instead of floor. Defaults to False.
"""
super(Model, self).__init__()
self.maxpool = nn.MaxPool3d(kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, return_indices=return_indices, ceil_mode=ceil_mode)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Max Pooling 3D to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, channels, dim1, dim2, dim3).
Returns:
torch.Tensor: Output tensor with Max Pooling 3D applied.
"""
return self.maxpool(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.
| 0.095511 |
max_pooling_1d
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return torch.nn.functional.conv2d(x.unsqueeze(0), weight, stride=1).squeeze(0)
C_in, C_out, H, W, KH, KW = 3, 8, 32, 32, 3, 3
def get_inputs():
x = torch.randn(C_in, H, W)
y = torch.randn(C_out, C_in, KH, KW)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name conv2d**:
The transformtion includes three parts: `conv2d_kernel` function, `conv2d` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
import time
# -----------------------------
# Triton 2D Convolution Kernel
# -----------------------------
@triton.jit
def conv2d_kernel(
output_ptr, input_ptr, weight_ptr,
H: tl.constexpr, W: tl.constexpr, # Input height and width
KH: tl.constexpr, KW: tl.constexpr, # Kernel height and width
IC: tl.constexpr, OC: tl.constexpr, # Input/output channels
stride_h: tl.constexpr, stride_w: tl.constexpr,
BLOCK_H: tl.constexpr, BLOCK_W: tl.constexpr
):
pid_h = tl.program_id(0)
pid_w = tl.program_id(1)
for oc in range(OC):
for oh in range(BLOCK_H):
for ow in range(BLOCK_W):
h = pid_h * BLOCK_H + oh
w = pid_w * BLOCK_W + ow
acc = 0.0
if h < H - KH + 1 and w < W - KW + 1:
for ic in range(IC):
for kh in range(KH):
for kw in range(KW):
x = tl.load(input_ptr + ic*H*W + (h+kh)*W + (w+kw))
k = tl.load(weight_ptr + oc*IC*KH*KW + ic*KH*KW + kh*KW + kw)
acc += x * k
tl.store(output_ptr + oc*(H-KH+1)*(W-KW+1) + h*(W-KW+1) + w, acc)
# -----------------------------
# Python wrapper
# -----------------------------
def conv2d(x, weight, stride=(1,1), block_size=(4,4)):
"""
x: (C_in, H, W)
weight: (C_out, C_in, KH, KW)
"""
C_in, H, W = x.shape
C_out, _, KH, KW = weight.shape
SH, SW = stride
out_H = (H - KH) // SH + 1
out_W = (W - KW) // SW + 1
y = torch.empty((C_out, out_H, out_W), device=x.device, dtype=x.dtype)
grid = (triton.cdiv(out_H, block_size[0]), triton.cdiv(out_W, block_size[1]))
conv2d_kernel[grid](
y, x, weight,
H, W, KH, KW, C_in, C_out,
SH, SW,
block_size[0], block_size[1]
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
stride: tuple = (1,1),
block_size: tuple = (2,2)
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
stride (tuple): Stride for the convolution (stride_h, stride_w).
block_size (tuple): Block size for Triton kernel (BLOCK_H, BLOCK_W).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return conv2d(x, weight, stride, block_size)
```
Now, you are given the following PyTorch architecture with name max_pooling_1d
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs Max Pooling 1D.
"""
def __init__(self, kernel_size: int, stride: int = None, padding: int = 0, dilation: int = 1, return_indices: bool = False):
"""
Initializes the Max Pooling 1D layer.
Args:
kernel_size (int): Size of the window to take a max over.
stride (int, optional): Stride of the window. Defaults to None (same as kernel_size).
padding (int, optional): Implicit zero padding to be added on both sides. Defaults to 0.
dilation (int, optional): Spacing between kernel elements. Defaults to 1.
return_indices (bool, optional): Whether to return the indices of the maximum values. Defaults to False.
"""
super(Model, self).__init__()
self.maxpool = nn.MaxPool1d(kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, return_indices=return_indices)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Max Pooling 1D to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features, sequence_length).
Returns:
torch.Tensor: Output tensor with Max Pooling 1D applied, shape (batch_size, num_features, output_sequence_length).
"""
return self.maxpool(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.
| 0.002416 |
average_pooling_1d
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return torch.nn.functional.conv2d(x.unsqueeze(0), weight, stride=1).squeeze(0)
C_in, C_out, H, W, KH, KW = 3, 8, 32, 32, 3, 3
def get_inputs():
x = torch.randn(C_in, H, W)
y = torch.randn(C_out, C_in, KH, KW)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name conv2d**:
The transformtion includes three parts: `conv2d_kernel` function, `conv2d` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
import time
# -----------------------------
# Triton 2D Convolution Kernel
# -----------------------------
@triton.jit
def conv2d_kernel(
output_ptr, input_ptr, weight_ptr,
H: tl.constexpr, W: tl.constexpr, # Input height and width
KH: tl.constexpr, KW: tl.constexpr, # Kernel height and width
IC: tl.constexpr, OC: tl.constexpr, # Input/output channels
stride_h: tl.constexpr, stride_w: tl.constexpr,
BLOCK_H: tl.constexpr, BLOCK_W: tl.constexpr
):
pid_h = tl.program_id(0)
pid_w = tl.program_id(1)
for oc in range(OC):
for oh in range(BLOCK_H):
for ow in range(BLOCK_W):
h = pid_h * BLOCK_H + oh
w = pid_w * BLOCK_W + ow
acc = 0.0
if h < H - KH + 1 and w < W - KW + 1:
for ic in range(IC):
for kh in range(KH):
for kw in range(KW):
x = tl.load(input_ptr + ic*H*W + (h+kh)*W + (w+kw))
k = tl.load(weight_ptr + oc*IC*KH*KW + ic*KH*KW + kh*KW + kw)
acc += x * k
tl.store(output_ptr + oc*(H-KH+1)*(W-KW+1) + h*(W-KW+1) + w, acc)
# -----------------------------
# Python wrapper
# -----------------------------
def conv2d(x, weight, stride=(1,1), block_size=(4,4)):
"""
x: (C_in, H, W)
weight: (C_out, C_in, KH, KW)
"""
C_in, H, W = x.shape
C_out, _, KH, KW = weight.shape
SH, SW = stride
out_H = (H - KH) // SH + 1
out_W = (W - KW) // SW + 1
y = torch.empty((C_out, out_H, out_W), device=x.device, dtype=x.dtype)
grid = (triton.cdiv(out_H, block_size[0]), triton.cdiv(out_W, block_size[1]))
conv2d_kernel[grid](
y, x, weight,
H, W, KH, KW, C_in, C_out,
SH, SW,
block_size[0], block_size[1]
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
stride: tuple = (1,1),
block_size: tuple = (2,2)
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
stride (tuple): Stride for the convolution (stride_h, stride_w).
block_size (tuple): Block size for Triton kernel (BLOCK_H, BLOCK_W).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return conv2d(x, weight, stride, block_size)
```
Now, you are given the following PyTorch architecture with name average_pooling_1d
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs 1D Average Pooling.
"""
def __init__(self, kernel_size: int, stride: int = 1, padding: int = 0):
"""
Initializes the 1D Average Pooling layer.
Args:
kernel_size (int): Size of the pooling window.
stride (int, optional): Stride of the pooling operation. Defaults to 1.
padding (int, optional): Padding applied to the input tensor. Defaults to 0.
"""
super(Model, self).__init__()
self.avg_pool = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=padding)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies 1D Average Pooling to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, in_channels, input_length).
Returns:
torch.Tensor: Output tensor with 1D Average Pooling applied, shape (batch_size, in_channels, output_length).
"""
return self.avg_pool(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.
| 0.000181 |
average_pooling_3d
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return torch.nn.functional.conv2d(x.unsqueeze(0), weight, stride=1).squeeze(0)
C_in, C_out, H, W, KH, KW = 3, 8, 32, 32, 3, 3
def get_inputs():
x = torch.randn(C_in, H, W)
y = torch.randn(C_out, C_in, KH, KW)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name conv2d**:
The transformtion includes three parts: `conv2d_kernel` function, `conv2d` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
import time
# -----------------------------
# Triton 2D Convolution Kernel
# -----------------------------
@triton.jit
def conv2d_kernel(
output_ptr, input_ptr, weight_ptr,
H: tl.constexpr, W: tl.constexpr, # Input height and width
KH: tl.constexpr, KW: tl.constexpr, # Kernel height and width
IC: tl.constexpr, OC: tl.constexpr, # Input/output channels
stride_h: tl.constexpr, stride_w: tl.constexpr,
BLOCK_H: tl.constexpr, BLOCK_W: tl.constexpr
):
pid_h = tl.program_id(0)
pid_w = tl.program_id(1)
for oc in range(OC):
for oh in range(BLOCK_H):
for ow in range(BLOCK_W):
h = pid_h * BLOCK_H + oh
w = pid_w * BLOCK_W + ow
acc = 0.0
if h < H - KH + 1 and w < W - KW + 1:
for ic in range(IC):
for kh in range(KH):
for kw in range(KW):
x = tl.load(input_ptr + ic*H*W + (h+kh)*W + (w+kw))
k = tl.load(weight_ptr + oc*IC*KH*KW + ic*KH*KW + kh*KW + kw)
acc += x * k
tl.store(output_ptr + oc*(H-KH+1)*(W-KW+1) + h*(W-KW+1) + w, acc)
# -----------------------------
# Python wrapper
# -----------------------------
def conv2d(x, weight, stride=(1,1), block_size=(4,4)):
"""
x: (C_in, H, W)
weight: (C_out, C_in, KH, KW)
"""
C_in, H, W = x.shape
C_out, _, KH, KW = weight.shape
SH, SW = stride
out_H = (H - KH) // SH + 1
out_W = (W - KW) // SW + 1
y = torch.empty((C_out, out_H, out_W), device=x.device, dtype=x.dtype)
grid = (triton.cdiv(out_H, block_size[0]), triton.cdiv(out_W, block_size[1]))
conv2d_kernel[grid](
y, x, weight,
H, W, KH, KW, C_in, C_out,
SH, SW,
block_size[0], block_size[1]
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
stride: tuple = (1,1),
block_size: tuple = (2,2)
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
stride (tuple): Stride for the convolution (stride_h, stride_w).
block_size (tuple): Block size for Triton kernel (BLOCK_H, BLOCK_W).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return conv2d(x, weight, stride, block_size)
```
Now, you are given the following PyTorch architecture with name average_pooling_3d
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs 3D Average Pooling.
"""
def __init__(self, kernel_size: int, stride: int = None, padding: int = 0):
"""
Initializes the Average Pooling layer.
Args:
kernel_size (int): Size of the kernel to apply pooling.
stride (int, optional): Stride of the pooling operation. Defaults to None, which uses the kernel size.
padding (int, optional): Padding to apply before pooling. Defaults to 0.
"""
super(Model, self).__init__()
self.avg_pool = nn.AvgPool3d(kernel_size=kernel_size, stride=stride, padding=padding)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Average Pooling to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, channels, depth, height, width).
Returns:
torch.Tensor: Output tensor with Average Pooling applied, shape depends on kernel_size, stride and padding.
"""
return self.avg_pool(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.
| 0.052774 |
average_pooling_2d
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return torch.nn.functional.conv2d(x.unsqueeze(0), weight, stride=1).squeeze(0)
C_in, C_out, H, W, KH, KW = 3, 8, 32, 32, 3, 3
def get_inputs():
x = torch.randn(C_in, H, W)
y = torch.randn(C_out, C_in, KH, KW)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name conv2d**:
The transformtion includes three parts: `conv2d_kernel` function, `conv2d` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
import time
# -----------------------------
# Triton 2D Convolution Kernel
# -----------------------------
@triton.jit
def conv2d_kernel(
output_ptr, input_ptr, weight_ptr,
H: tl.constexpr, W: tl.constexpr, # Input height and width
KH: tl.constexpr, KW: tl.constexpr, # Kernel height and width
IC: tl.constexpr, OC: tl.constexpr, # Input/output channels
stride_h: tl.constexpr, stride_w: tl.constexpr,
BLOCK_H: tl.constexpr, BLOCK_W: tl.constexpr
):
pid_h = tl.program_id(0)
pid_w = tl.program_id(1)
for oc in range(OC):
for oh in range(BLOCK_H):
for ow in range(BLOCK_W):
h = pid_h * BLOCK_H + oh
w = pid_w * BLOCK_W + ow
acc = 0.0
if h < H - KH + 1 and w < W - KW + 1:
for ic in range(IC):
for kh in range(KH):
for kw in range(KW):
x = tl.load(input_ptr + ic*H*W + (h+kh)*W + (w+kw))
k = tl.load(weight_ptr + oc*IC*KH*KW + ic*KH*KW + kh*KW + kw)
acc += x * k
tl.store(output_ptr + oc*(H-KH+1)*(W-KW+1) + h*(W-KW+1) + w, acc)
# -----------------------------
# Python wrapper
# -----------------------------
def conv2d(x, weight, stride=(1,1), block_size=(4,4)):
"""
x: (C_in, H, W)
weight: (C_out, C_in, KH, KW)
"""
C_in, H, W = x.shape
C_out, _, KH, KW = weight.shape
SH, SW = stride
out_H = (H - KH) // SH + 1
out_W = (W - KW) // SW + 1
y = torch.empty((C_out, out_H, out_W), device=x.device, dtype=x.dtype)
grid = (triton.cdiv(out_H, block_size[0]), triton.cdiv(out_W, block_size[1]))
conv2d_kernel[grid](
y, x, weight,
H, W, KH, KW, C_in, C_out,
SH, SW,
block_size[0], block_size[1]
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
stride: tuple = (1,1),
block_size: tuple = (2,2)
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
stride (tuple): Stride for the convolution (stride_h, stride_w).
block_size (tuple): Block size for Triton kernel (BLOCK_H, BLOCK_W).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return conv2d(x, weight, stride, block_size)
```
Now, you are given the following PyTorch architecture with name average_pooling_2d
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs 2D Average Pooling.
"""
def __init__(self, kernel_size: int, stride: int = None, padding: int = 0):
"""
Initializes the Average Pooling layer.
Args:
kernel_size (int): Size of the pooling window.
stride (int, optional): Stride of the pooling operation. Defaults to None (same as kernel_size).
padding (int, optional): Padding applied to the input tensor. Defaults to 0.
"""
super(Model, self).__init__()
self.avg_pool = nn.AvgPool2d(kernel_size=kernel_size, stride=stride, padding=padding)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies 2D Average Pooling to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, channels, height, width).
Returns:
torch.Tensor: Output tensor with Average Pooling applied.
"""
return self.avg_pool(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.
| 0.017813 |
sum_reduction_over_a_dimension
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Input shape from an expected input of size
(*, normalized_shape). It defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): A value added to the denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return nn.functional.layer_norm(x, normalized_shape, weight, bias, eps)
batch_size = 16
features = 64
dim1 = 256
dim2 = 256
def get_inputs():
x = torch.randn(batch_size, features, dim1, dim2)
normalized_shape = (features, dim1, dim2)
weight = torch.ones(normalized_shape)
bias = torch.zeros(normalized_shape)
eps = 1e-5
return [x, normalized_shape, weight, bias, eps]
def get_init_inputs():
return []
```
**Transformed Triton Architecture with name layer_norm**:
The transformtion includes three parts: `layer_norm_kernel` function, `layer_norm` 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 _layer_norm_kernel(
X, # Input pointer
Y, # Output pointer
W, # Weight pointer
B, # Bias pointer
Mean, # Mean pointer
Rstd, # 1/std pointer
stride, # How much to move the pointer per row
N, # Number of columns in X
eps, # Epsilon to avoid division by zero
BLOCK_SIZE: tl.constexpr,
):
# Map program id to the corresponding row of X and Y
row = tl.program_id(0)
Y += row * stride
X += row * stride
# Compute mean
mean = 0
_mean = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
a = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
_mean += a
mean = tl.sum(_mean, axis=0) / N
# Compute variance
_var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
x = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
x = tl.where(cols < N, x - mean, 0.)
_var += x * x
var = tl.sum(_var, axis=0) / N
rstd = 1 / tl.sqrt(var + eps)
# Store mean and rstd
tl.store(Mean + row, mean)
tl.store(Rstd + row, rstd)
# Normalize and apply linear transformation
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
mask = cols < N
w = tl.load(W + cols, mask=mask)
b = tl.load(B + cols, mask=mask)
x = tl.load(X + cols, mask=mask, other=0.).to(tl.float32)
x_hat = (x - mean) * rstd
y = x_hat * w + b
# Write output
tl.store(Y + cols, y, mask=mask)
@torch.inference_mode()
def layer_norm(x, normalized_shape, weight, bias, eps=1e-5):
# Allocate output tensor with the same shape and dtype as input
y = torch.empty_like(x)
# Reshape input x to 2D shape [-1, feature_dim] to normalize the last dimension
x_arg = x.reshape(-1, x.shape[-1])
M, N = x_arg.shape
mean = torch.empty((M, ), dtype=torch.float32, device=x.device)
rstd = torch.empty((M, ), dtype=torch.float32, device=x.device)
BLOCK_SIZE = 1024
# Launch kernel
kernel = _layer_norm_kernel[(M, )]( # M is the number of blocks, launch grid=(M,)
x_arg, y, weight, bias, mean, rstd, # Inputs, outputs, and intermediates
x_arg.stride(0), N, eps,
BLOCK_SIZE=BLOCK_SIZE)
# Return normalized output
return y
class ModelNew(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Expected input shape (*, normalized_shape). Defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): Value added to denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return layer_norm(x, normalized_shape, weight, bias, eps)
```
Now, you are given the following PyTorch architecture with name sum_reduction_over_a_dimension
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs sum reduction over a specified dimension.
"""
def __init__(self, dim: int):
"""
Initializes the model with the dimension to reduce over.
Args:
dim (int): Dimension to reduce over.
"""
super(Model, self).__init__()
self.dim = dim
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies sum reduction over the specified dimension.
Args:
x (torch.Tensor): Input tensor of shape (..., dim, ...).
Returns:
torch.Tensor: Output tensor after sum reduction, shape (..., 1, ...).
"""
return torch.sum(x, dim=self.dim, keepdim=True)
```
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 |
max_reduction_over_a_dimension
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Input shape from an expected input of size
(*, normalized_shape). It defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): A value added to the denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return nn.functional.layer_norm(x, normalized_shape, weight, bias, eps)
batch_size = 16
features = 64
dim1 = 256
dim2 = 256
def get_inputs():
x = torch.randn(batch_size, features, dim1, dim2)
normalized_shape = (features, dim1, dim2)
weight = torch.ones(normalized_shape)
bias = torch.zeros(normalized_shape)
eps = 1e-5
return [x, normalized_shape, weight, bias, eps]
def get_init_inputs():
return []
```
**Transformed Triton Architecture with name layer_norm**:
The transformtion includes three parts: `layer_norm_kernel` function, `layer_norm` 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 _layer_norm_kernel(
X, # Input pointer
Y, # Output pointer
W, # Weight pointer
B, # Bias pointer
Mean, # Mean pointer
Rstd, # 1/std pointer
stride, # How much to move the pointer per row
N, # Number of columns in X
eps, # Epsilon to avoid division by zero
BLOCK_SIZE: tl.constexpr,
):
# Map program id to the corresponding row of X and Y
row = tl.program_id(0)
Y += row * stride
X += row * stride
# Compute mean
mean = 0
_mean = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
a = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
_mean += a
mean = tl.sum(_mean, axis=0) / N
# Compute variance
_var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
x = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
x = tl.where(cols < N, x - mean, 0.)
_var += x * x
var = tl.sum(_var, axis=0) / N
rstd = 1 / tl.sqrt(var + eps)
# Store mean and rstd
tl.store(Mean + row, mean)
tl.store(Rstd + row, rstd)
# Normalize and apply linear transformation
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
mask = cols < N
w = tl.load(W + cols, mask=mask)
b = tl.load(B + cols, mask=mask)
x = tl.load(X + cols, mask=mask, other=0.).to(tl.float32)
x_hat = (x - mean) * rstd
y = x_hat * w + b
# Write output
tl.store(Y + cols, y, mask=mask)
@torch.inference_mode()
def layer_norm(x, normalized_shape, weight, bias, eps=1e-5):
# Allocate output tensor with the same shape and dtype as input
y = torch.empty_like(x)
# Reshape input x to 2D shape [-1, feature_dim] to normalize the last dimension
x_arg = x.reshape(-1, x.shape[-1])
M, N = x_arg.shape
mean = torch.empty((M, ), dtype=torch.float32, device=x.device)
rstd = torch.empty((M, ), dtype=torch.float32, device=x.device)
BLOCK_SIZE = 1024
# Launch kernel
kernel = _layer_norm_kernel[(M, )]( # M is the number of blocks, launch grid=(M,)
x_arg, y, weight, bias, mean, rstd, # Inputs, outputs, and intermediates
x_arg.stride(0), N, eps,
BLOCK_SIZE=BLOCK_SIZE)
# Return normalized output
return y
class ModelNew(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Expected input shape (*, normalized_shape). Defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): Value added to denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return layer_norm(x, normalized_shape, weight, bias, eps)
```
Now, you are given the following PyTorch architecture with name max_reduction_over_a_dimension
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs Max reduction over a specific dimension.
"""
def __init__(self, dim: int):
"""
Initializes the model with the dimension to reduce over.
Args:
dim (int): The dimension to reduce over.
"""
super(Model, self).__init__()
self.dim = dim
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Max reduction over the specified dimension to the input tensor.
Args:
x (torch.Tensor): Input tensor.
Returns:
torch.Tensor: Output tensor after Max reduction over the specified dimension.
"""
return torch.max(x, dim=self.dim)[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.
| 0.00022 |
mean_reduction_over_a_dimension
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Input shape from an expected input of size
(*, normalized_shape). It defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): A value added to the denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return nn.functional.layer_norm(x, normalized_shape, weight, bias, eps)
batch_size = 16
features = 64
dim1 = 256
dim2 = 256
def get_inputs():
x = torch.randn(batch_size, features, dim1, dim2)
normalized_shape = (features, dim1, dim2)
weight = torch.ones(normalized_shape)
bias = torch.zeros(normalized_shape)
eps = 1e-5
return [x, normalized_shape, weight, bias, eps]
def get_init_inputs():
return []
```
**Transformed Triton Architecture with name layer_norm**:
The transformtion includes three parts: `layer_norm_kernel` function, `layer_norm` 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 _layer_norm_kernel(
X, # Input pointer
Y, # Output pointer
W, # Weight pointer
B, # Bias pointer
Mean, # Mean pointer
Rstd, # 1/std pointer
stride, # How much to move the pointer per row
N, # Number of columns in X
eps, # Epsilon to avoid division by zero
BLOCK_SIZE: tl.constexpr,
):
# Map program id to the corresponding row of X and Y
row = tl.program_id(0)
Y += row * stride
X += row * stride
# Compute mean
mean = 0
_mean = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
a = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
_mean += a
mean = tl.sum(_mean, axis=0) / N
# Compute variance
_var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
x = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
x = tl.where(cols < N, x - mean, 0.)
_var += x * x
var = tl.sum(_var, axis=0) / N
rstd = 1 / tl.sqrt(var + eps)
# Store mean and rstd
tl.store(Mean + row, mean)
tl.store(Rstd + row, rstd)
# Normalize and apply linear transformation
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
mask = cols < N
w = tl.load(W + cols, mask=mask)
b = tl.load(B + cols, mask=mask)
x = tl.load(X + cols, mask=mask, other=0.).to(tl.float32)
x_hat = (x - mean) * rstd
y = x_hat * w + b
# Write output
tl.store(Y + cols, y, mask=mask)
@torch.inference_mode()
def layer_norm(x, normalized_shape, weight, bias, eps=1e-5):
# Allocate output tensor with the same shape and dtype as input
y = torch.empty_like(x)
# Reshape input x to 2D shape [-1, feature_dim] to normalize the last dimension
x_arg = x.reshape(-1, x.shape[-1])
M, N = x_arg.shape
mean = torch.empty((M, ), dtype=torch.float32, device=x.device)
rstd = torch.empty((M, ), dtype=torch.float32, device=x.device)
BLOCK_SIZE = 1024
# Launch kernel
kernel = _layer_norm_kernel[(M, )]( # M is the number of blocks, launch grid=(M,)
x_arg, y, weight, bias, mean, rstd, # Inputs, outputs, and intermediates
x_arg.stride(0), N, eps,
BLOCK_SIZE=BLOCK_SIZE)
# Return normalized output
return y
class ModelNew(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Expected input shape (*, normalized_shape). Defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): Value added to denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return layer_norm(x, normalized_shape, weight, bias, eps)
```
Now, you are given the following PyTorch architecture with name mean_reduction_over_a_dimension
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs mean reduction over a specific dimension.
"""
def __init__(self, dim: int):
"""
Initializes the model with the dimension to reduce over.
Args:
dim (int): The dimension to reduce over.
"""
super(Model, self).__init__()
self.dim = dim
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Reduces the input tensor along the specified dimension by taking the mean.
Args:
x (torch.Tensor): Input tensor of arbitrary shape.
Returns:
torch.Tensor: Output tensor with reduced dimension. The shape of the output is the same as the input except for the reduced dimension which is removed.
"""
return torch.mean(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.
| 0.000304 |
product_reduction_over_a_dimension
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name layer_norm**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Input shape from an expected input of size
(*, normalized_shape). It defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): A value added to the denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return nn.functional.layer_norm(x, normalized_shape, weight, bias, eps)
batch_size = 16
features = 64
dim1 = 256
dim2 = 256
def get_inputs():
x = torch.randn(batch_size, features, dim1, dim2)
normalized_shape = (features, dim1, dim2)
weight = torch.ones(normalized_shape)
bias = torch.zeros(normalized_shape)
eps = 1e-5
return [x, normalized_shape, weight, bias, eps]
def get_init_inputs():
return []
```
**Transformed Triton Architecture with name layer_norm**:
The transformtion includes three parts: `layer_norm_kernel` function, `layer_norm` 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 _layer_norm_kernel(
X, # Input pointer
Y, # Output pointer
W, # Weight pointer
B, # Bias pointer
Mean, # Mean pointer
Rstd, # 1/std pointer
stride, # How much to move the pointer per row
N, # Number of columns in X
eps, # Epsilon to avoid division by zero
BLOCK_SIZE: tl.constexpr,
):
# Map program id to the corresponding row of X and Y
row = tl.program_id(0)
Y += row * stride
X += row * stride
# Compute mean
mean = 0
_mean = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
a = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
_mean += a
mean = tl.sum(_mean, axis=0) / N
# Compute variance
_var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
x = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
x = tl.where(cols < N, x - mean, 0.)
_var += x * x
var = tl.sum(_var, axis=0) / N
rstd = 1 / tl.sqrt(var + eps)
# Store mean and rstd
tl.store(Mean + row, mean)
tl.store(Rstd + row, rstd)
# Normalize and apply linear transformation
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
mask = cols < N
w = tl.load(W + cols, mask=mask)
b = tl.load(B + cols, mask=mask)
x = tl.load(X + cols, mask=mask, other=0.).to(tl.float32)
x_hat = (x - mean) * rstd
y = x_hat * w + b
# Write output
tl.store(Y + cols, y, mask=mask)
@torch.inference_mode()
def layer_norm(x, normalized_shape, weight, bias, eps=1e-5):
# Allocate output tensor with the same shape and dtype as input
y = torch.empty_like(x)
# Reshape input x to 2D shape [-1, feature_dim] to normalize the last dimension
x_arg = x.reshape(-1, x.shape[-1])
M, N = x_arg.shape
mean = torch.empty((M, ), dtype=torch.float32, device=x.device)
rstd = torch.empty((M, ), dtype=torch.float32, device=x.device)
BLOCK_SIZE = 1024
# Launch kernel
kernel = _layer_norm_kernel[(M, )]( # M is the number of blocks, launch grid=(M,)
x_arg, y, weight, bias, mean, rstd, # Inputs, outputs, and intermediates
x_arg.stride(0), N, eps,
BLOCK_SIZE=BLOCK_SIZE)
# Return normalized output
return y
class ModelNew(nn.Module):
"""
Simple model that performs Layer Normalization.
"""
def __init__(self):
"""
Initializes the LayerNorm layer.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
normalized_shape: tuple,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float
) -> torch.Tensor:
"""
Applies Layer Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (*, normalized_shape).
normalized_shape (tuple): Expected input shape (*, normalized_shape). Defines the axes over which normalization is applied.
weight (torch.Tensor): Learnable scale parameter of shape `normalized_shape`.
bias (torch.Tensor): Learnable shift parameter of shape `normalized_shape`.
eps (float): Value added to denominator for numerical stability.
Returns:
torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
"""
return layer_norm(x, normalized_shape, weight, bias, eps)
```
Now, you are given the following PyTorch architecture with name product_reduction_over_a_dimension
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs product reduction over a dimension.
"""
def __init__(self, dim: int):
"""
Initializes the model with the dimension to reduce over.
Args:
dim (int): Dimension to reduce over.
"""
super(Model, self).__init__()
self.dim = dim
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Performs product reduction over the specified dimension.
Args:
x (torch.Tensor): Input tensor.
Returns:
torch.Tensor: Output tensor with product reduction applied.
"""
return torch.prod(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 |
upsample_grid_sample
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return torch.nn.functional.conv2d(x.unsqueeze(0), weight, stride=1).squeeze(0)
C_in, C_out, H, W, KH, KW = 3, 8, 32, 32, 3, 3
def get_inputs():
x = torch.randn(C_in, H, W)
y = torch.randn(C_out, C_in, KH, KW)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name conv2d**:
The transformtion includes three parts: `conv2d_kernel` function, `conv2d` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
import time
# -----------------------------
# Triton 2D Convolution Kernel
# -----------------------------
@triton.jit
def conv2d_kernel(
output_ptr, input_ptr, weight_ptr,
H: tl.constexpr, W: tl.constexpr, # Input height and width
KH: tl.constexpr, KW: tl.constexpr, # Kernel height and width
IC: tl.constexpr, OC: tl.constexpr, # Input/output channels
stride_h: tl.constexpr, stride_w: tl.constexpr,
BLOCK_H: tl.constexpr, BLOCK_W: tl.constexpr
):
pid_h = tl.program_id(0)
pid_w = tl.program_id(1)
for oc in range(OC):
for oh in range(BLOCK_H):
for ow in range(BLOCK_W):
h = pid_h * BLOCK_H + oh
w = pid_w * BLOCK_W + ow
acc = 0.0
if h < H - KH + 1 and w < W - KW + 1:
for ic in range(IC):
for kh in range(KH):
for kw in range(KW):
x = tl.load(input_ptr + ic*H*W + (h+kh)*W + (w+kw))
k = tl.load(weight_ptr + oc*IC*KH*KW + ic*KH*KW + kh*KW + kw)
acc += x * k
tl.store(output_ptr + oc*(H-KH+1)*(W-KW+1) + h*(W-KW+1) + w, acc)
# -----------------------------
# Python wrapper
# -----------------------------
def conv2d(x, weight, stride=(1,1), block_size=(4,4)):
"""
x: (C_in, H, W)
weight: (C_out, C_in, KH, KW)
"""
C_in, H, W = x.shape
C_out, _, KH, KW = weight.shape
SH, SW = stride
out_H = (H - KH) // SH + 1
out_W = (W - KW) // SW + 1
y = torch.empty((C_out, out_H, out_W), device=x.device, dtype=x.dtype)
grid = (triton.cdiv(out_H, block_size[0]), triton.cdiv(out_W, block_size[1]))
conv2d_kernel[grid](
y, x, weight,
H, W, KH, KW, C_in, C_out,
SH, SW,
block_size[0], block_size[1]
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
stride: tuple = (1,1),
block_size: tuple = (2,2)
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
stride (tuple): Stride for the convolution (stride_h, stride_w).
block_size (tuple): Block size for Triton kernel (BLOCK_H, BLOCK_W).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return conv2d(x, weight, stride, block_size)
```
Now, you are given the following PyTorch architecture with name upsample_grid_sample
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def forward(self, x, theta):
x_up = F.interpolate(x, scale_factor=2.0, mode='bilinear', align_corners=False)
grid = F.affine_grid(theta, x_up.size(), align_corners=False)
return F.grid_sample(x_up, grid, mode='bilinear', align_corners=False)
```
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.
| 0.013539 |
nearest_neighbor_upsample
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return torch.nn.functional.conv2d(x.unsqueeze(0), weight, stride=1).squeeze(0)
C_in, C_out, H, W, KH, KW = 3, 8, 32, 32, 3, 3
def get_inputs():
x = torch.randn(C_in, H, W)
y = torch.randn(C_out, C_in, KH, KW)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name conv2d**:
The transformtion includes three parts: `conv2d_kernel` function, `conv2d` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
import time
# -----------------------------
# Triton 2D Convolution Kernel
# -----------------------------
@triton.jit
def conv2d_kernel(
output_ptr, input_ptr, weight_ptr,
H: tl.constexpr, W: tl.constexpr, # Input height and width
KH: tl.constexpr, KW: tl.constexpr, # Kernel height and width
IC: tl.constexpr, OC: tl.constexpr, # Input/output channels
stride_h: tl.constexpr, stride_w: tl.constexpr,
BLOCK_H: tl.constexpr, BLOCK_W: tl.constexpr
):
pid_h = tl.program_id(0)
pid_w = tl.program_id(1)
for oc in range(OC):
for oh in range(BLOCK_H):
for ow in range(BLOCK_W):
h = pid_h * BLOCK_H + oh
w = pid_w * BLOCK_W + ow
acc = 0.0
if h < H - KH + 1 and w < W - KW + 1:
for ic in range(IC):
for kh in range(KH):
for kw in range(KW):
x = tl.load(input_ptr + ic*H*W + (h+kh)*W + (w+kw))
k = tl.load(weight_ptr + oc*IC*KH*KW + ic*KH*KW + kh*KW + kw)
acc += x * k
tl.store(output_ptr + oc*(H-KH+1)*(W-KW+1) + h*(W-KW+1) + w, acc)
# -----------------------------
# Python wrapper
# -----------------------------
def conv2d(x, weight, stride=(1,1), block_size=(4,4)):
"""
x: (C_in, H, W)
weight: (C_out, C_in, KH, KW)
"""
C_in, H, W = x.shape
C_out, _, KH, KW = weight.shape
SH, SW = stride
out_H = (H - KH) // SH + 1
out_W = (W - KW) // SW + 1
y = torch.empty((C_out, out_H, out_W), device=x.device, dtype=x.dtype)
grid = (triton.cdiv(out_H, block_size[0]), triton.cdiv(out_W, block_size[1]))
conv2d_kernel[grid](
y, x, weight,
H, W, KH, KW, C_in, C_out,
SH, SW,
block_size[0], block_size[1]
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
stride: tuple = (1,1),
block_size: tuple = (2,2)
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
stride (tuple): Stride for the convolution (stride_h, stride_w).
block_size (tuple): Block size for Triton kernel (BLOCK_H, BLOCK_W).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return conv2d(x, weight, stride, block_size)
```
Now, you are given the following PyTorch architecture with name nearest_neighbor_upsample
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def forward(self, x):
return F.interpolate(x, scale_factor=4.0, mode='nearest')
```
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.
| 0.048744 |
grid_sample_affine
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return torch.nn.functional.conv2d(x.unsqueeze(0), weight, stride=1).squeeze(0)
C_in, C_out, H, W, KH, KW = 3, 8, 32, 32, 3, 3
def get_inputs():
x = torch.randn(C_in, H, W)
y = torch.randn(C_out, C_in, KH, KW)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name conv2d**:
The transformtion includes three parts: `conv2d_kernel` function, `conv2d` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
import time
# -----------------------------
# Triton 2D Convolution Kernel
# -----------------------------
@triton.jit
def conv2d_kernel(
output_ptr, input_ptr, weight_ptr,
H: tl.constexpr, W: tl.constexpr, # Input height and width
KH: tl.constexpr, KW: tl.constexpr, # Kernel height and width
IC: tl.constexpr, OC: tl.constexpr, # Input/output channels
stride_h: tl.constexpr, stride_w: tl.constexpr,
BLOCK_H: tl.constexpr, BLOCK_W: tl.constexpr
):
pid_h = tl.program_id(0)
pid_w = tl.program_id(1)
for oc in range(OC):
for oh in range(BLOCK_H):
for ow in range(BLOCK_W):
h = pid_h * BLOCK_H + oh
w = pid_w * BLOCK_W + ow
acc = 0.0
if h < H - KH + 1 and w < W - KW + 1:
for ic in range(IC):
for kh in range(KH):
for kw in range(KW):
x = tl.load(input_ptr + ic*H*W + (h+kh)*W + (w+kw))
k = tl.load(weight_ptr + oc*IC*KH*KW + ic*KH*KW + kh*KW + kw)
acc += x * k
tl.store(output_ptr + oc*(H-KH+1)*(W-KW+1) + h*(W-KW+1) + w, acc)
# -----------------------------
# Python wrapper
# -----------------------------
def conv2d(x, weight, stride=(1,1), block_size=(4,4)):
"""
x: (C_in, H, W)
weight: (C_out, C_in, KH, KW)
"""
C_in, H, W = x.shape
C_out, _, KH, KW = weight.shape
SH, SW = stride
out_H = (H - KH) // SH + 1
out_W = (W - KW) // SW + 1
y = torch.empty((C_out, out_H, out_W), device=x.device, dtype=x.dtype)
grid = (triton.cdiv(out_H, block_size[0]), triton.cdiv(out_W, block_size[1]))
conv2d_kernel[grid](
y, x, weight,
H, W, KH, KW, C_in, C_out,
SH, SW,
block_size[0], block_size[1]
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
stride: tuple = (1,1),
block_size: tuple = (2,2)
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
stride (tuple): Stride for the convolution (stride_h, stride_w).
block_size (tuple): Block size for Triton kernel (BLOCK_H, BLOCK_W).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return conv2d(x, weight, stride, block_size)
```
Now, you are given the following PyTorch architecture with name grid_sample_affine
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def forward(self, x, theta):
grid = F.affine_grid(theta, x.size(), align_corners=False)
return F.grid_sample(x, grid, mode='bilinear', align_corners=False)
```
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.
| 0.029867 |
grid_sample_random_warp
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return torch.nn.functional.conv2d(x.unsqueeze(0), weight, stride=1).squeeze(0)
C_in, C_out, H, W, KH, KW = 3, 8, 32, 32, 3, 3
def get_inputs():
x = torch.randn(C_in, H, W)
y = torch.randn(C_out, C_in, KH, KW)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name conv2d**:
The transformtion includes three parts: `conv2d_kernel` function, `conv2d` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
import time
# -----------------------------
# Triton 2D Convolution Kernel
# -----------------------------
@triton.jit
def conv2d_kernel(
output_ptr, input_ptr, weight_ptr,
H: tl.constexpr, W: tl.constexpr, # Input height and width
KH: tl.constexpr, KW: tl.constexpr, # Kernel height and width
IC: tl.constexpr, OC: tl.constexpr, # Input/output channels
stride_h: tl.constexpr, stride_w: tl.constexpr,
BLOCK_H: tl.constexpr, BLOCK_W: tl.constexpr
):
pid_h = tl.program_id(0)
pid_w = tl.program_id(1)
for oc in range(OC):
for oh in range(BLOCK_H):
for ow in range(BLOCK_W):
h = pid_h * BLOCK_H + oh
w = pid_w * BLOCK_W + ow
acc = 0.0
if h < H - KH + 1 and w < W - KW + 1:
for ic in range(IC):
for kh in range(KH):
for kw in range(KW):
x = tl.load(input_ptr + ic*H*W + (h+kh)*W + (w+kw))
k = tl.load(weight_ptr + oc*IC*KH*KW + ic*KH*KW + kh*KW + kw)
acc += x * k
tl.store(output_ptr + oc*(H-KH+1)*(W-KW+1) + h*(W-KW+1) + w, acc)
# -----------------------------
# Python wrapper
# -----------------------------
def conv2d(x, weight, stride=(1,1), block_size=(4,4)):
"""
x: (C_in, H, W)
weight: (C_out, C_in, KH, KW)
"""
C_in, H, W = x.shape
C_out, _, KH, KW = weight.shape
SH, SW = stride
out_H = (H - KH) // SH + 1
out_W = (W - KW) // SW + 1
y = torch.empty((C_out, out_H, out_W), device=x.device, dtype=x.dtype)
grid = (triton.cdiv(out_H, block_size[0]), triton.cdiv(out_W, block_size[1]))
conv2d_kernel[grid](
y, x, weight,
H, W, KH, KW, C_in, C_out,
SH, SW,
block_size[0], block_size[1]
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
stride: tuple = (1,1),
block_size: tuple = (2,2)
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
stride (tuple): Stride for the convolution (stride_h, stride_w).
block_size (tuple): Block size for Triton kernel (BLOCK_H, BLOCK_W).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return conv2d(x, weight, stride, block_size)
```
Now, you are given the following PyTorch architecture with name grid_sample_random_warp
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def forward(self, x, grid):
return F.grid_sample(x, grid, mode='bilinear', align_corners=False)
```
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.
| 0.003833 |
bicubic_upsample
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return torch.nn.functional.conv2d(x.unsqueeze(0), weight, stride=1).squeeze(0)
C_in, C_out, H, W, KH, KW = 3, 8, 32, 32, 3, 3
def get_inputs():
x = torch.randn(C_in, H, W)
y = torch.randn(C_out, C_in, KH, KW)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name conv2d**:
The transformtion includes three parts: `conv2d_kernel` function, `conv2d` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
import time
# -----------------------------
# Triton 2D Convolution Kernel
# -----------------------------
@triton.jit
def conv2d_kernel(
output_ptr, input_ptr, weight_ptr,
H: tl.constexpr, W: tl.constexpr, # Input height and width
KH: tl.constexpr, KW: tl.constexpr, # Kernel height and width
IC: tl.constexpr, OC: tl.constexpr, # Input/output channels
stride_h: tl.constexpr, stride_w: tl.constexpr,
BLOCK_H: tl.constexpr, BLOCK_W: tl.constexpr
):
pid_h = tl.program_id(0)
pid_w = tl.program_id(1)
for oc in range(OC):
for oh in range(BLOCK_H):
for ow in range(BLOCK_W):
h = pid_h * BLOCK_H + oh
w = pid_w * BLOCK_W + ow
acc = 0.0
if h < H - KH + 1 and w < W - KW + 1:
for ic in range(IC):
for kh in range(KH):
for kw in range(KW):
x = tl.load(input_ptr + ic*H*W + (h+kh)*W + (w+kw))
k = tl.load(weight_ptr + oc*IC*KH*KW + ic*KH*KW + kh*KW + kw)
acc += x * k
tl.store(output_ptr + oc*(H-KH+1)*(W-KW+1) + h*(W-KW+1) + w, acc)
# -----------------------------
# Python wrapper
# -----------------------------
def conv2d(x, weight, stride=(1,1), block_size=(4,4)):
"""
x: (C_in, H, W)
weight: (C_out, C_in, KH, KW)
"""
C_in, H, W = x.shape
C_out, _, KH, KW = weight.shape
SH, SW = stride
out_H = (H - KH) // SH + 1
out_W = (W - KW) // SW + 1
y = torch.empty((C_out, out_H, out_W), device=x.device, dtype=x.dtype)
grid = (triton.cdiv(out_H, block_size[0]), triton.cdiv(out_W, block_size[1]))
conv2d_kernel[grid](
y, x, weight,
H, W, KH, KW, C_in, C_out,
SH, SW,
block_size[0], block_size[1]
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
stride: tuple = (1,1),
block_size: tuple = (2,2)
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
stride (tuple): Stride for the convolution (stride_h, stride_w).
block_size (tuple): Block size for Triton kernel (BLOCK_H, BLOCK_W).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return conv2d(x, weight, stride, block_size)
```
Now, you are given the following PyTorch architecture with name bicubic_upsample
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def forward(self, x):
return F.interpolate(x, size=(256, 256), mode='bicubic', align_corners=True)
```
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.
| 0.026305 |
downsample_bilinear
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return torch.nn.functional.conv2d(x.unsqueeze(0), weight, stride=1).squeeze(0)
C_in, C_out, H, W, KH, KW = 3, 8, 32, 32, 3, 3
def get_inputs():
x = torch.randn(C_in, H, W)
y = torch.randn(C_out, C_in, KH, KW)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name conv2d**:
The transformtion includes three parts: `conv2d_kernel` function, `conv2d` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
import time
# -----------------------------
# Triton 2D Convolution Kernel
# -----------------------------
@triton.jit
def conv2d_kernel(
output_ptr, input_ptr, weight_ptr,
H: tl.constexpr, W: tl.constexpr, # Input height and width
KH: tl.constexpr, KW: tl.constexpr, # Kernel height and width
IC: tl.constexpr, OC: tl.constexpr, # Input/output channels
stride_h: tl.constexpr, stride_w: tl.constexpr,
BLOCK_H: tl.constexpr, BLOCK_W: tl.constexpr
):
pid_h = tl.program_id(0)
pid_w = tl.program_id(1)
for oc in range(OC):
for oh in range(BLOCK_H):
for ow in range(BLOCK_W):
h = pid_h * BLOCK_H + oh
w = pid_w * BLOCK_W + ow
acc = 0.0
if h < H - KH + 1 and w < W - KW + 1:
for ic in range(IC):
for kh in range(KH):
for kw in range(KW):
x = tl.load(input_ptr + ic*H*W + (h+kh)*W + (w+kw))
k = tl.load(weight_ptr + oc*IC*KH*KW + ic*KH*KW + kh*KW + kw)
acc += x * k
tl.store(output_ptr + oc*(H-KH+1)*(W-KW+1) + h*(W-KW+1) + w, acc)
# -----------------------------
# Python wrapper
# -----------------------------
def conv2d(x, weight, stride=(1,1), block_size=(4,4)):
"""
x: (C_in, H, W)
weight: (C_out, C_in, KH, KW)
"""
C_in, H, W = x.shape
C_out, _, KH, KW = weight.shape
SH, SW = stride
out_H = (H - KH) // SH + 1
out_W = (W - KW) // SW + 1
y = torch.empty((C_out, out_H, out_W), device=x.device, dtype=x.dtype)
grid = (triton.cdiv(out_H, block_size[0]), triton.cdiv(out_W, block_size[1]))
conv2d_kernel[grid](
y, x, weight,
H, W, KH, KW, C_in, C_out,
SH, SW,
block_size[0], block_size[1]
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
stride: tuple = (1,1),
block_size: tuple = (2,2)
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
stride (tuple): Stride for the convolution (stride_h, stride_w).
block_size (tuple): Block size for Triton kernel (BLOCK_H, BLOCK_W).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return conv2d(x, weight, stride, block_size)
```
Now, you are given the following PyTorch architecture with name downsample_bilinear
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def forward(self, x):
return F.interpolate(x, size=(60, 80), mode='bilinear', align_corners=False)
```
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.
| 0.004658 |
resize_with_antialias
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return torch.nn.functional.conv2d(x.unsqueeze(0), weight, stride=1).squeeze(0)
C_in, C_out, H, W, KH, KW = 3, 8, 32, 32, 3, 3
def get_inputs():
x = torch.randn(C_in, H, W)
y = torch.randn(C_out, C_in, KH, KW)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name conv2d**:
The transformtion includes three parts: `conv2d_kernel` function, `conv2d` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
import time
# -----------------------------
# Triton 2D Convolution Kernel
# -----------------------------
@triton.jit
def conv2d_kernel(
output_ptr, input_ptr, weight_ptr,
H: tl.constexpr, W: tl.constexpr, # Input height and width
KH: tl.constexpr, KW: tl.constexpr, # Kernel height and width
IC: tl.constexpr, OC: tl.constexpr, # Input/output channels
stride_h: tl.constexpr, stride_w: tl.constexpr,
BLOCK_H: tl.constexpr, BLOCK_W: tl.constexpr
):
pid_h = tl.program_id(0)
pid_w = tl.program_id(1)
for oc in range(OC):
for oh in range(BLOCK_H):
for ow in range(BLOCK_W):
h = pid_h * BLOCK_H + oh
w = pid_w * BLOCK_W + ow
acc = 0.0
if h < H - KH + 1 and w < W - KW + 1:
for ic in range(IC):
for kh in range(KH):
for kw in range(KW):
x = tl.load(input_ptr + ic*H*W + (h+kh)*W + (w+kw))
k = tl.load(weight_ptr + oc*IC*KH*KW + ic*KH*KW + kh*KW + kw)
acc += x * k
tl.store(output_ptr + oc*(H-KH+1)*(W-KW+1) + h*(W-KW+1) + w, acc)
# -----------------------------
# Python wrapper
# -----------------------------
def conv2d(x, weight, stride=(1,1), block_size=(4,4)):
"""
x: (C_in, H, W)
weight: (C_out, C_in, KH, KW)
"""
C_in, H, W = x.shape
C_out, _, KH, KW = weight.shape
SH, SW = stride
out_H = (H - KH) // SH + 1
out_W = (W - KW) // SW + 1
y = torch.empty((C_out, out_H, out_W), device=x.device, dtype=x.dtype)
grid = (triton.cdiv(out_H, block_size[0]), triton.cdiv(out_W, block_size[1]))
conv2d_kernel[grid](
y, x, weight,
H, W, KH, KW, C_in, C_out,
SH, SW,
block_size[0], block_size[1]
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
stride: tuple = (1,1),
block_size: tuple = (2,2)
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
stride (tuple): Stride for the convolution (stride_h, stride_w).
block_size (tuple): Block size for Triton kernel (BLOCK_H, BLOCK_W).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return conv2d(x, weight, stride, block_size)
```
Now, you are given the following PyTorch architecture with name resize_with_antialias
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def forward(self, x):
return F.interpolate(x, size=(128, 128), mode='bilinear', align_corners=False, antialias=True)
```
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.
| 0.001044 |
interpolate_dynamic
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return torch.nn.functional.conv2d(x.unsqueeze(0), weight, stride=1).squeeze(0)
C_in, C_out, H, W, KH, KW = 3, 8, 32, 32, 3, 3
def get_inputs():
x = torch.randn(C_in, H, W)
y = torch.randn(C_out, C_in, KH, KW)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name conv2d**:
The transformtion includes three parts: `conv2d_kernel` function, `conv2d` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
import time
# -----------------------------
# Triton 2D Convolution Kernel
# -----------------------------
@triton.jit
def conv2d_kernel(
output_ptr, input_ptr, weight_ptr,
H: tl.constexpr, W: tl.constexpr, # Input height and width
KH: tl.constexpr, KW: tl.constexpr, # Kernel height and width
IC: tl.constexpr, OC: tl.constexpr, # Input/output channels
stride_h: tl.constexpr, stride_w: tl.constexpr,
BLOCK_H: tl.constexpr, BLOCK_W: tl.constexpr
):
pid_h = tl.program_id(0)
pid_w = tl.program_id(1)
for oc in range(OC):
for oh in range(BLOCK_H):
for ow in range(BLOCK_W):
h = pid_h * BLOCK_H + oh
w = pid_w * BLOCK_W + ow
acc = 0.0
if h < H - KH + 1 and w < W - KW + 1:
for ic in range(IC):
for kh in range(KH):
for kw in range(KW):
x = tl.load(input_ptr + ic*H*W + (h+kh)*W + (w+kw))
k = tl.load(weight_ptr + oc*IC*KH*KW + ic*KH*KW + kh*KW + kw)
acc += x * k
tl.store(output_ptr + oc*(H-KH+1)*(W-KW+1) + h*(W-KW+1) + w, acc)
# -----------------------------
# Python wrapper
# -----------------------------
def conv2d(x, weight, stride=(1,1), block_size=(4,4)):
"""
x: (C_in, H, W)
weight: (C_out, C_in, KH, KW)
"""
C_in, H, W = x.shape
C_out, _, KH, KW = weight.shape
SH, SW = stride
out_H = (H - KH) // SH + 1
out_W = (W - KW) // SW + 1
y = torch.empty((C_out, out_H, out_W), device=x.device, dtype=x.dtype)
grid = (triton.cdiv(out_H, block_size[0]), triton.cdiv(out_W, block_size[1]))
conv2d_kernel[grid](
y, x, weight,
H, W, KH, KW, C_in, C_out,
SH, SW,
block_size[0], block_size[1]
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
stride: tuple = (1,1),
block_size: tuple = (2,2)
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
stride (tuple): Stride for the convolution (stride_h, stride_w).
block_size (tuple): Block size for Triton kernel (BLOCK_H, BLOCK_W).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return conv2d(x, weight, stride, block_size)
```
Now, you are given the following PyTorch architecture with name interpolate_dynamic
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def forward(self, x, target_size):
return F.interpolate(x, size=target_size, mode='bilinear', align_corners=False)
```
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.
| 0.0468 |
trilinear_upsample
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return torch.nn.functional.conv2d(x.unsqueeze(0), weight, stride=1).squeeze(0)
C_in, C_out, H, W, KH, KW = 3, 8, 32, 32, 3, 3
def get_inputs():
x = torch.randn(C_in, H, W)
y = torch.randn(C_out, C_in, KH, KW)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name conv2d**:
The transformtion includes three parts: `conv2d_kernel` function, `conv2d` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
import time
# -----------------------------
# Triton 2D Convolution Kernel
# -----------------------------
@triton.jit
def conv2d_kernel(
output_ptr, input_ptr, weight_ptr,
H: tl.constexpr, W: tl.constexpr, # Input height and width
KH: tl.constexpr, KW: tl.constexpr, # Kernel height and width
IC: tl.constexpr, OC: tl.constexpr, # Input/output channels
stride_h: tl.constexpr, stride_w: tl.constexpr,
BLOCK_H: tl.constexpr, BLOCK_W: tl.constexpr
):
pid_h = tl.program_id(0)
pid_w = tl.program_id(1)
for oc in range(OC):
for oh in range(BLOCK_H):
for ow in range(BLOCK_W):
h = pid_h * BLOCK_H + oh
w = pid_w * BLOCK_W + ow
acc = 0.0
if h < H - KH + 1 and w < W - KW + 1:
for ic in range(IC):
for kh in range(KH):
for kw in range(KW):
x = tl.load(input_ptr + ic*H*W + (h+kh)*W + (w+kw))
k = tl.load(weight_ptr + oc*IC*KH*KW + ic*KH*KW + kh*KW + kw)
acc += x * k
tl.store(output_ptr + oc*(H-KH+1)*(W-KW+1) + h*(W-KW+1) + w, acc)
# -----------------------------
# Python wrapper
# -----------------------------
def conv2d(x, weight, stride=(1,1), block_size=(4,4)):
"""
x: (C_in, H, W)
weight: (C_out, C_in, KH, KW)
"""
C_in, H, W = x.shape
C_out, _, KH, KW = weight.shape
SH, SW = stride
out_H = (H - KH) // SH + 1
out_W = (W - KW) // SW + 1
y = torch.empty((C_out, out_H, out_W), device=x.device, dtype=x.dtype)
grid = (triton.cdiv(out_H, block_size[0]), triton.cdiv(out_W, block_size[1]))
conv2d_kernel[grid](
y, x, weight,
H, W, KH, KW, C_in, C_out,
SH, SW,
block_size[0], block_size[1]
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
stride: tuple = (1,1),
block_size: tuple = (2,2)
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
stride (tuple): Stride for the convolution (stride_h, stride_w).
block_size (tuple): Block size for Triton kernel (BLOCK_H, BLOCK_W).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return conv2d(x, weight, stride, block_size)
```
Now, you are given the following PyTorch architecture with name trilinear_upsample
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def forward(self, x):
return F.interpolate(x, scale_factor=(2, 2, 2), mode='trilinear', align_corners=True)
```
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.
| 0.017522 |
bilinear_upsample
|
You are an expert in writing custom AscendC Triton Kernels to optimize PyTorch architectures by replacing specific operators for performance gains.
Here is an example to illustrate the expected transformation using custom AscendC Triton kernels.
**Original PyTorch Architecture with name conv2d**:
```python
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(Model, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return torch.nn.functional.conv2d(x.unsqueeze(0), weight, stride=1).squeeze(0)
C_in, C_out, H, W, KH, KW = 3, 8, 32, 32, 3, 3
def get_inputs():
x = torch.randn(C_in, H, W)
y = torch.randn(C_out, C_in, KH, KW)
return [x, y]
def get_init_inputs():
return [] # No special initialization inputs needed
```
**Transformed Triton Architecture with name conv2d**:
The transformtion includes three parts: `conv2d_kernel` function, `conv2d` afunction, and `ModelNew` class.
```python
import torch
import torch.nn as nn
import torch_npu
import triton
import triton.language as tl
import time
# -----------------------------
# Triton 2D Convolution Kernel
# -----------------------------
@triton.jit
def conv2d_kernel(
output_ptr, input_ptr, weight_ptr,
H: tl.constexpr, W: tl.constexpr, # Input height and width
KH: tl.constexpr, KW: tl.constexpr, # Kernel height and width
IC: tl.constexpr, OC: tl.constexpr, # Input/output channels
stride_h: tl.constexpr, stride_w: tl.constexpr,
BLOCK_H: tl.constexpr, BLOCK_W: tl.constexpr
):
pid_h = tl.program_id(0)
pid_w = tl.program_id(1)
for oc in range(OC):
for oh in range(BLOCK_H):
for ow in range(BLOCK_W):
h = pid_h * BLOCK_H + oh
w = pid_w * BLOCK_W + ow
acc = 0.0
if h < H - KH + 1 and w < W - KW + 1:
for ic in range(IC):
for kh in range(KH):
for kw in range(KW):
x = tl.load(input_ptr + ic*H*W + (h+kh)*W + (w+kw))
k = tl.load(weight_ptr + oc*IC*KH*KW + ic*KH*KW + kh*KW + kw)
acc += x * k
tl.store(output_ptr + oc*(H-KH+1)*(W-KW+1) + h*(W-KW+1) + w, acc)
# -----------------------------
# Python wrapper
# -----------------------------
def conv2d(x, weight, stride=(1,1), block_size=(4,4)):
"""
x: (C_in, H, W)
weight: (C_out, C_in, KH, KW)
"""
C_in, H, W = x.shape
C_out, _, KH, KW = weight.shape
SH, SW = stride
out_H = (H - KH) // SH + 1
out_W = (W - KW) // SW + 1
y = torch.empty((C_out, out_H, out_W), device=x.device, dtype=x.dtype)
grid = (triton.cdiv(out_H, block_size[0]), triton.cdiv(out_W, block_size[1]))
conv2d_kernel[grid](
y, x, weight,
H, W, KH, KW, C_in, C_out,
SH, SW,
block_size[0], block_size[1]
)
return y
class ModelNew(nn.Module):
"""
Simple model that performs 2D Convolution using Triton.
"""
def __init__(self):
"""
Initializes the Model.
"""
super(ModelNew, self).__init__()
def forward(self,
x: torch.Tensor,
weight: torch.Tensor,
stride: tuple = (1,1),
block_size: tuple = (2,2)
) -> torch.Tensor:
"""
Applies 2D Convolution to the input tensor using Triton kernel.
Args:
x (torch.Tensor): Input tensor of shape (C_in, H, W).
weight (torch.Tensor): Convolution kernel of shape (C_out, C_in, KH, KW).
stride (tuple): Stride for the convolution (stride_h, stride_w).
block_size (tuple): Block size for Triton kernel (BLOCK_H, BLOCK_W).
Returns:
torch.Tensor: Output tensor after convolution of shape (C_out, out_H, out_W).
"""
return conv2d(x, weight, stride, block_size)
```
Now, you are given the following PyTorch architecture with name bilinear_upsample
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def forward(self, x):
return F.interpolate(x, scale_factor=2.0, mode='bilinear', align_corners=False)
```
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.
| 0.050005 |
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