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[GH-PAGES] Updated website
2022-02-09 07:15:50 +00:00
<EFBFBD><05><>l<00>sphinx.addnodes<65><73>document<6E><74><EFBFBD>)<29><>}<7D>(<28> rawsource<63><65><00><>children<65>]<5D>(<28>docutils.nodes<65><73>comment<6E><74><EFBFBD>)<29><>}<7D>(h<05> DO NOT EDIT.<2E>h]<5D>h <09>Text<78><74><EFBFBD><EFBFBD> DO NOT EDIT.<2E><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hh<06>parent<6E>h uba<62>
attributes<EFBFBD>}<7D>(<28>ids<64>]<5D><>classes<65>]<5D><>names<65>]<5D><>dupnames<65>]<5D><>backrefs<66>]<5D><> xml:space<63><65>preserve<76>u<EFBFBD>tagname<6D>h
[GH-PAGES] Updated website
2022-08-04 00:49:04 +00:00
hhhh<03>source<63><65>j/tmp/tmpv9mhi1e6/2d6df9b518a8152f777eb79b6b0a84becb706353/docs/getting-started/tutorials/01-vector-add.rst<73><74>line<6E>Kubh )<29><>}<7D>(h<05>8THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.<2E>h]<5D>h<11>8THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.<2E><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhh)ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$uh%h
[GH-PAGES] Updated website
2022-02-09 07:15:50 +00:00
hhhhh&h'h(Kubh )<29><>}<7D>(h<05>-TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:<3A>h]<5D>h<11>-TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:<3A><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhh7ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$uh%h
hhhhh&h'h(Kubh )<29><>}<7D>(h<05>,"getting-started/tutorials/01-vector-add.py"<22>h]<5D>h<11>,"getting-started/tutorials/01-vector-add.py"<22><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhhEubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$uh%h
hhhhh&h'h(Kubh )<29><>}<7D>(h<05>LINE NUMBERS ARE GIVEN BELOW.<2E>h]<5D>h<11>LINE NUMBERS ARE GIVEN BELOW.<2E><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhhSubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$uh%h
hhhhh&h'h(Kubh<00>only<6C><79><EFBFBD>)<29><>}<7D>(hhh]<5D>h <09>note<74><65><EFBFBD>)<29><>}<7D>(h<05>rClick :ref:`here <sphx_glr_download_getting-started_tutorials_01-vector-add.py>`
to download the full example code<64>h]<5D>h <09> paragraph<70><68><EFBFBD>)<29><>}<7D>(h<05>rClick :ref:`here <sphx_glr_download_getting-started_tutorials_01-vector-add.py>`
to download the full example code<64>h]<5D>(h<11>Click <20><><EFBFBD><EFBFBD><EFBFBD>}<7D>(h<05>Click <20>hhnubh<00> pending_xref<65><66><EFBFBD>)<29><>}<7D>(h<05>J:ref:`here <sphx_glr_download_getting-started_tutorials_01-vector-add.py>`<60>h]<5D>h <09>inline<6E><65><EFBFBD>)<29><>}<7D>(hh{h]<5D>h<11>here<72><65><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhhubah}<7D>(h]<5D>h]<5D>(<28>xref<65><66>std<74><64>std-ref<65>eh]<5D>h]<5D>h!]<5D>uh%h}hhyubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D><>refdoc<6F><63>'getting-started/tutorials/01-vector-add<64><64> refdomain<69>h<EFBFBD><68>reftype<70><65>ref<65><66> refexplicit<69><74><EFBFBD>refwarn<72><6E><EFBFBD> reftarget<65><74><sphx_glr_download_getting-started_tutorials_01-vector-add.py<70>uh%hwh&h'h(K hhnubh<11>"
to download the full example code<64><65><EFBFBD><EFBFBD><EFBFBD>}<7D>(h<05>"
to download the full example code<64>hhnubeh}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%hlh&h'h(K hhhubah}<7D>(h]<5D>h]<5D><>sphx-glr-download-link-note<74>ah]<5D>h]<5D>h!]<5D>uh%hfhhchhh&h'h(Nubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D><>expr<70><72>html<6D>uh%hahhh&h'h(Khhubh <09>target<65><74><EFBFBD>)<29><>}<7D>(h<05>8.. _sphx_glr_getting-started_tutorials_01-vector-add.py:<3A>h]<5D>h}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D><>refid<69><64>3sphx-glr-getting-started-tutorials-01-vector-add-py<70>uh%h<>h(Khhhhh&h'ubh <09>section<6F><6E><EFBFBD>)<29><>}<7D>(hhh]<5D>(h <09>title<6C><65><EFBFBD>)<29><>}<7D>(h<05>Vector Addition<6F>h]<5D>h<11>Vector Addition<6F><6E><EFBFBD><EFBFBD><EFBFBD>}<7D>(hh<>hh<>hhh&Nh(Nubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%h<>hh<>hhh&h'h(Kubhm)<29><>}<7D>(h<05>WIn this tutorial, you will write a simple vector addition using Triton and learn about:<3A>h]<5D>h<11>WIn this tutorial, you will write a simple vector addition using Triton and learn about:<3A><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hh<>hh<>hhh&Nh(Nubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%hlh&h'h(Khh<>hhubh <09> bullet_list<73><74><EFBFBD>)<29><>}<7D>(hhh]<5D>(h <09> list_item<65><6D><EFBFBD>)<29><>}<7D>(h<05>%The basic programming model of Triton<6F>h]<5D>hm)<29><>}<7D>(hh<>h]<5D>h<11>%The basic programming model of Triton<6F><6E><EFBFBD><EFBFBD><EFBFBD>}<7D>(hh<>hh<>ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%hlh&h'h(Khh<>ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%h<>hh<>hhh&h'h(Nubh<62>)<29><>}<7D>(h<05>CThe `triton.jit` decorator, which is used to define Triton kernels.<2E>h]<5D>hm)<29><>}<7D>(hj h]<5D>(h<11>The <20><><EFBFBD><EFBFBD><EFBFBD>}<7D>(h<05>The <20>hj ubh <09>title_reference<63><65><EFBFBD>)<29><>}<7D>(h<05> `triton.jit`<60>h]<5D>h<11>
triton.jit<69><74><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhjubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%jhj ubh<11>3 decorator, which is used to define Triton kernels.<2E><><EFBFBD><EFBFBD><EFBFBD>}<7D>(h<05>3 decorator, which is used to define Triton kernels.<2E>hj ubeh}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%hlh&h'h(Khjubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%h<>hh<>hhh&h'h(Nubh<62>)<29><>}<7D>(h<05>lThe best practices for validating and benchmarking your custom ops against native reference implementations
<EFBFBD>h]<5D>hm)<29><>}<7D>(h<05>kThe best practices for validating and benchmarking your custom ops against native reference implementations<6E>h]<5D>h<11>kThe best practices for validating and benchmarking your custom ops against native reference implementations<6E><73><EFBFBD><EFBFBD><EFBFBD>}<7D>(hj:hj8ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%hlh&h'h(Khj4ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%h<>hh<>hhh&h'h(Nubeh}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D><>bullet<65><74>-<2D>uh%h<>h&h'h(Khh<>hhubh )<29><>}<7D>(h<05>(GENERATED FROM PYTHON SOURCE LINES 12-14<31>h]<5D>h<11>(GENERATED FROM PYTHON SOURCE LINES 12-14<31><34><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhjTubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$uh%h
hh<>hhh&h'h(Kubh<62>)<29><>}<7D>(hhh]<5D>(h<>)<29><>}<7D>(h<05>Compute Kernel<65>h]<5D>h<11>Compute Kernel<65><6C><EFBFBD><EFBFBD><EFBFBD>}<7D>(hjghjehhh&Nh(Nubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%h<>hjbhhh&h'h(K ubh )<29><>}<7D>(h<05>(GENERATED FROM PYTHON SOURCE LINES 14-49<34>h]<5D>h<11>(GENERATED FROM PYTHON SOURCE LINES 14-49<34><39><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhjsubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$uh%h
hjbhhh&h'h(K#ubh <09> literal_block<63><6B><EFBFBD>)<29><>}<7D>(hXaimport torch
import triton
import triton.language as tl
@triton.jit
def add_kernel(
x_ptr, # *Pointer* to first input vector
y_ptr, # *Pointer* to second input vector
output_ptr, # *Pointer* to output vector
n_elements, # Size of the vector
**meta, # Optional meta-parameters for the kernel
):
BLOCK_SIZE = meta['BLOCK_SIZE'] # How many inputs each program should process
# There are multiple 'program's processing different data. We identify which program
# we are here
pid = tl.program_id(axis=0) # We use a 1D launch grid so axis is 0
# This program will process inputs that are offset from the initial data.
# for instance, if you had a vector of length 256 and block_size of 64, the programs
# would each access the elements [0:64, 64:128, 128:192, 192:256].
# Note that offsets is a list of pointers
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
# Create a mask to guard memory operations against out-of-bounds accesses
mask = offsets < n_elements
# Load x and y from DRAM, masking out any extar elements in case the input is not a
# multiple of the block size
x = tl.load(x_ptr + offsets, mask=mask)
y = tl.load(y_ptr + offsets, mask=mask)
output = x + y
# Write x + y back to DRAM
tl.store(output_ptr + offsets, output, mask=mask)<29>h]<5D>hXaimport torch
import triton
import triton.language as tl
@triton.jit
def add_kernel(
x_ptr, # *Pointer* to first input vector
y_ptr, # *Pointer* to second input vector
output_ptr, # *Pointer* to output vector
n_elements, # Size of the vector
**meta, # Optional meta-parameters for the kernel
):
BLOCK_SIZE = meta['BLOCK_SIZE'] # How many inputs each program should process
# There are multiple 'program's processing different data. We identify which program
# we are here
pid = tl.program_id(axis=0) # We use a 1D launch grid so axis is 0
# This program will process inputs that are offset from the initial data.
# for instance, if you had a vector of length 256 and block_size of 64, the programs
# would each access the elements [0:64, 64:128, 128:192, 192:256].
# Note that offsets is a list of pointers
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
# Create a mask to guard memory operations against out-of-bounds accesses
mask = offsets < n_elements
# Load x and y from DRAM, masking out any extar elements in case the input is not a
# multiple of the block size
x = tl.load(x_ptr + offsets, mask=mask)
y = tl.load(y_ptr + offsets, mask=mask)
output = x + y
# Write x + y back to DRAM
tl.store(output_ptr + offsets, output, mask=mask)<29><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhj<>ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$<24>force<63><65><EFBFBD>language<67><65>default<6C><74>highlight_args<67>}<7D>uh%j<>h&h'h(K$hjbhhubh )<29><>}<7D>(h<05>(GENERATED FROM PYTHON SOURCE LINES 50-52<35>h]<5D>h<11>(GENERATED FROM PYTHON SOURCE LINES 50-52<35><32><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhj<>ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$uh%h
hjbhhh&h'h(KQubhm)<29><>}<7D>(h<05><>Let's also declare a helper function to (1) allocate the `z` tensor
and (2) enqueue the above kernel with appropriate grid/block sizes.<2E>h]<5D>(h<11>;Let’s also declare a helper function to (1) allocate the <20><><EFBFBD><EFBFBD><EFBFBD>}<7D>(h<05>9Let's also declare a helper function to (1) allocate the <20>hj<>hhh&Nh(Nubj)<29><>}<7D>(h<05>`z`<60>h]<5D>h<11>z<><7A><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhj<>ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%jhj<>ubh<11>K tensor
and (2) enqueue the above kernel with appropriate grid/block sizes.<2E><><EFBFBD><EFBFBD><EFBFBD>}<7D>(h<05>K tensor
and (2) enqueue the above kernel with appropriate grid/block sizes.<2E>hj<>hhh&Nh(Nubeh}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%hlh&h'h(KRhjbhhubh )<29><>}<7D>(h<05>(GENERATED FROM PYTHON SOURCE LINES 52-73<37>h]<5D>h<11>(GENERATED FROM PYTHON SOURCE LINES 52-73<37><33><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhj<>ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$uh%h
hjbhhh&h'h(KVubj<62>)<29><>}<7D>(hX)def add(x: torch.Tensor, y: torch.Tensor):
# We need to preallocate the output
output = torch.empty_like(x)
assert x.is_cuda and y.is_cuda and output.is_cuda
n_elements = output.numel()
# The SPMD launch grid denotes the number of kernel instances that run in parallel.
# It is analogous to CUDA launch grids. It can be either Tuple[int], or Callable(metaparameters) -> Tuple[int]
# In this case, we use a 1D grid where the size is the number of blocks
grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
# NOTE:
# - each torch.tensor object is implicitly converted into a pointer to its first element.
# - `triton.jit`'ed functions can be index with a launch grid to obtain a callable GPU kernel
# - don't forget to pass meta-parameters as keywords arguments
pgm = add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=1024)
# We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still
# running asynchronously at this point.
return output<75>h]<5D>hX)def add(x: torch.Tensor, y: torch.Tensor):
# We need to preallocate the output
output = torch.empty_like(x)
assert x.is_cuda and y.is_cuda and output.is_cuda
n_elements = output.numel()
# The SPMD launch grid denotes the number of kernel instances that run in parallel.
# It is analogous to CUDA launch grids. It can be either Tuple[int], or Callable(metaparameters) -> Tuple[int]
# In this case, we use a 1D grid where the size is the number of blocks
grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
# NOTE:
# - each torch.tensor object is implicitly converted into a pointer to its first element.
# - `triton.jit`'ed functions can be index with a launch grid to obtain a callable GPU kernel
# - don't forget to pass meta-parameters as keywords arguments
pgm = add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=1024)
# We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still
# running asynchronously at this point.
return output<75><74><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhj<>ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$j<><00>j<EFBFBD><00>default<6C>j<EFBFBD>}<7D>uh%j<>h&h'h(KWhjbhhubh )<29><>}<7D>(h<05>(GENERATED FROM PYTHON SOURCE LINES 74-75<37>h]<5D>h<11>(GENERATED FROM PYTHON SOURCE LINES 74-75<37><35><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhj<>ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$uh%h
hjbhhh&h'h(Kvubhm)<29><>}<7D>(h<05>yWe can now use the above function to compute the element-wise sum of two `torch.tensor` objects and test its correctness:<3A>h]<5D>(h<11>IWe can now use the above function to compute the element-wise sum of two <20><><EFBFBD><EFBFBD><EFBFBD>}<7D>(h<05>IWe can now use the above function to compute the element-wise sum of two <20>hj<>hhh&Nh(Nubj)<29><>}<7D>(h<05>`torch.tensor`<60>h]<5D>h<11> torch.tensor<6F><72><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhj<>ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%jhj<>ubh<11>" objects and test its correctness:<3A><><EFBFBD><EFBFBD><EFBFBD>}<7D>(h<05>" objects and test its correctness:<3A>hj<>hhh&Nh(Nubeh}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%hlh&h'h(Kwhjbhhubh )<29><>}<7D>(h<05>(GENERATED FROM PYTHON SOURCE LINES 75-89<38>h]<5D>h<11>(GENERATED FROM PYTHON SOURCE LINES 75-89<38><39><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhjubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$uh%h
hjbhhh&h'h(Kzubj<62>)<29><>}<7D>(hXAtorch.manual_seed(0)
size = 98432
x = torch.rand(size, device='cuda')
y = torch.rand(size, device='cuda')
output_torch = x + y
output_triton = add(x, y)
print(output_torch)
print(output_triton)
print(
f'The maximum difference between torch and triton is '
f'{torch.max(torch.abs(output_torch - output_triton))}'
)<29>h]<5D>hXAtorch.manual_seed(0)
size = 98432
x = torch.rand(size, device='cuda')
y = torch.rand(size, device='cuda')
output_torch = x + y
output_triton = add(x, y)
print(output_torch)
print(output_triton)
print(
f'The maximum difference between torch and triton is '
f'{torch.max(torch.abs(output_torch - output_triton))}'
)<29><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhj"ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$j<><00>j<EFBFBD><00>default<6C>j<EFBFBD>}<7D>uh%j<>h&h'h(K{hjbhhubhm)<29><>}<7D>(h<05>Out:<3A>h]<5D>h<11>Out:<3A><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hj4hj2hhh&Nh(Nubah}<7D>(h]<5D>h]<5D><>sphx-glr-script-out<75>ah]<5D>h]<5D>h!]<5D>uh%hlh&h'h(K<>hjbhhubj<62>)<29><>}<7D>(h<05><>tensor([1.3713, 1.3076, 0.4940, ..., 0.6724, 1.2141, 0.9733], device='cuda:0')
tensor([1.3713, 1.3076, 0.4940, ..., 0.6724, 1.2141, 0.9733], device='cuda:0')
The maximum difference between torch and triton is 0.0<EFBFBD>h]<5D>h<11><>tensor([1.3713, 1.3076, 0.4940, ..., 0.6724, 1.2141, 0.9733], device='cuda:0')
tensor([1.3713, 1.3076, 0.4940, ..., 0.6724, 1.2141, 0.9733], device='cuda:0')
The maximum difference between torch and triton is 0.0<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhjAubah}<7D>(h]<5D>h]<5D>j=ah]<5D>h]<5D>h!]<5D>h#h$j<><00>j<EFBFBD><00>none<6E>j<EFBFBD>}<7D>uh%j<>h&h'h(K<>hjbhhubh )<29><>}<7D>(h<05>(GENERATED FROM PYTHON SOURCE LINES 90-91<39>h]<5D>h<11>(GENERATED FROM PYTHON SOURCE LINES 90-91<39><31><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhjQubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$uh%h
hjbhhh&h'h(K<>ubhm)<29><>}<7D>(h<05>Seems like we're good to go!<21>h]<5D>h<11>Seems like we’re good to go!<21><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hjahj_hhh&Nh(Nubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%hlh&h'h(K<>hjbhhubh )<29><>}<7D>(h<05>(GENERATED FROM PYTHON SOURCE LINES 93-98<39>h]<5D>h<11>(GENERATED FROM PYTHON SOURCE LINES 93-98<39><38><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhjmubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$uh%h
hjbhhh&h'h(K<>ubeh}<7D>(h]<5D><>compute-kernel<65>ah]<5D>h]<5D><>compute kernel<65>ah]<5D>h!]<5D>uh%h<>hh<>hhh&h'h(K ubh<62>)<29><>}<7D>(hhh]<5D>(h<>)<29><>}<7D>(h<05> Benchmark<72>h]<5D>h<11> Benchmark<72><6B><EFBFBD><EFBFBD><EFBFBD>}<7D>(hj<>hj<>hhh&Nh(Nubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%h<>hj<>hhh&h'h(K<>ubhm)<29><>}<7D>(hXWe can now benchmark our custom op on vectors of increasing sizes to get a sense of how it does relative to PyTorch.
To make things easier, Triton has a set of built-in utilities that allow us to concisely plot the performance of your custom ops
for different problem sizes.<2E>h]<5D>hXWe can now benchmark our custom op on vectors of increasing sizes to get a sense of how it does relative to PyTorch.
To make things easier, Triton has a set of built-in utilities that allow us to concisely plot the performance of your custom ops
for different problem sizes.<2E><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hj<>hj<>hhh&Nh(Nubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%hlh&h'h(K<>hj<>hhubh )<29><>}<7D>(h<05>)GENERATED FROM PYTHON SOURCE LINES 98-127<32>h]<5D>h<11>)GENERATED FROM PYTHON SOURCE LINES 98-127<32><37><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhj<>ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$uh%h
hj<>hhh&h'h(K<>ubj<62>)<29><>}<7D>(hX@triton.testing.perf_report(
triton.testing.Benchmark(
x_names=['size'], # argument names to use as an x-axis for the plot
x_vals=[
2 ** i for i in range(12, 28, 1)
], # different possible values for `x_name`
x_log=True, # x axis is logarithmic
line_arg='provider', # argument name whose value corresponds to a different line in the plot
line_vals=['triton', 'torch'], # possible values for `line_arg`
line_names=['Triton', 'Torch'], # label name for the lines
styles=[('blue', '-'), ('green', '-')], # line styles
ylabel='GB/s', # label name for the y-axis
plot_name='vector-add-performance', # name for the plot. Used also as a file name for saving the plot.
args={}, # values for function arguments not in `x_names` and `y_name`
)
)
def benchmark(size, provider):
x = torch.rand(size, device='cuda', dtype=torch.float32)
y = torch.rand(size, device='cuda', dtype=torch.float32)
if provider == 'torch':
ms, min_ms, max_ms = triton.testing.do_bench(lambda: x + y)
if provider == 'triton':
ms, min_ms, max_ms = triton.testing.do_bench(lambda: add(x, y))
gbps = lambda ms: 12 * size / ms * 1e-6
return gbps(ms), gbps(max_ms), gbps(min_ms)<29>h]<5D>hX@triton.testing.perf_report(
triton.testing.Benchmark(
x_names=['size'], # argument names to use as an x-axis for the plot
x_vals=[
2 ** i for i in range(12, 28, 1)
], # different possible values for `x_name`
x_log=True, # x axis is logarithmic
line_arg='provider', # argument name whose value corresponds to a different line in the plot
line_vals=['triton', 'torch'], # possible values for `line_arg`
line_names=['Triton', 'Torch'], # label name for the lines
styles=[('blue', '-'), ('green', '-')], # line styles
ylabel='GB/s', # label name for the y-axis
plot_name='vector-add-performance', # name for the plot. Used also as a file name for saving the plot.
args={}, # values for function arguments not in `x_names` and `y_name`
)
)
def benchmark(size, provider):
x = torch.rand(size, device='cuda', dtype=torch.float32)
y = torch.rand(size, device='cuda', dtype=torch.float32)
if provider == 'torch':
ms, min_ms, max_ms = triton.testing.do_bench(lambda: x + y)
if provider == 'triton':
ms, min_ms, max_ms = triton.testing.do_bench(lambda: add(x, y))
gbps = lambda ms: 12 * size / ms * 1e-6
return gbps(ms), gbps(max_ms), gbps(min_ms)<29><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhj<>ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$j<><00>j<EFBFBD><00>default<6C>j<EFBFBD>}<7D>uh%j<>h&h'h(K<>hj<>hhubh )<29><>}<7D>(h<05>*GENERATED FROM PYTHON SOURCE LINES 128-130<33>h]<5D>h<11>*GENERATED FROM PYTHON SOURCE LINES 128-130<33><30><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhj<>ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$uh%h
hj<>hhh&h'h(K<>ubhm)<29><>}<7D>(h<05><>We can now run the decorated function above. Pass `print_data=True` to see the performance number, `show_plots=True` to plot them, and/or
`save_path='/path/to/results/' to save them to disk along with raw CSV data<74>h]<5D>(h<11>2We can now run the decorated function above. Pass <20><><EFBFBD><EFBFBD><EFBFBD>}<7D>(h<05>2We can now run the decorated function above. Pass <20>hj<>hhh&Nh(Nubj)<29><>}<7D>(h<05>`print_data=True`<60>h]<5D>h<11>print_data=True<75><65><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhj<>ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%jhj<>ubh<11> to see the performance number, <20><><EFBFBD><EFBFBD><EFBFBD>}<7D>(h<05> to see the performance number, <20>hj<>hhh&Nh(Nubj)<29><>}<7D>(h<05>`show_plots=True`<60>h]<5D>h<11>show_plots=True<75><65><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhj<>ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%jhj<>ubh<11> to plot them, and/or
<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>}<7D>(h<05> to plot them, and/or
<EFBFBD>hj<>hhh&Nh(Nubh <09> problematic<69><63><EFBFBD>)<29><>}<7D>(h<05>`<60>h]<5D>h<11>`<60><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhj<>ubah}<7D>(h]<5D><>id2<64>ah]<5D>h]<5D>h]<5D>h!]<5D><>refid<69><64>id1<64>uh%j<>hj<>ubh<11>Nsave_path=’/path/to/results/’ to save them to disk along with raw CSV data<74><61><EFBFBD><EFBFBD><EFBFBD>}<7D>(h<05>Jsave_path='/path/to/results/' to save them to disk along with raw CSV data<74>hj<>hhh&Nh(Nubeh}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%hlh&h'h(K<>hj<>hhubh )<29><>}<7D>(h<05>*GENERATED FROM PYTHON SOURCE LINES 130-131<33>h]<5D>h<11>*GENERATED FROM PYTHON SOURCE LINES 130-131<33><31><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhjubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$uh%h
hj<>hhh&h'h(K<>ubj<62>)<29><>}<7D>(h<05>/benchmark.run(print_data=True, show_plots=True)<29>h]<5D>h<11>/benchmark.run(print_data=True, show_plots=True)<29><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhj)ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$j<><00>j<EFBFBD><00>default<6C>j<EFBFBD>}<7D>uh%j<>h&h'h(K<>hj<>hhubh <09>image<67><65><EFBFBD>)<29><>}<7D>(h<05><>.. image:: /getting-started/tutorials/images/sphx_glr_01-vector-add_001.png
:alt: 01 vector add
:class: sphx-glr-single-img
<EFBFBD>h]<5D>h}<7D>(h]<5D>h]<5D><>sphx-glr-single-img<6D>ah]<5D>h]<5D>h!]<5D><>alt<6C><74> 01 vector add<64><64>uri<72><69>?getting-started/tutorials/images/sphx_glr_01-vector-add_001.png<6E><67>
candidates<EFBFBD>}<7D><>*<2A>jIsuh%j9hj<>hhh&h'h(Nubhm)<29><>}<7D>(h<05>Out:<3A>h]<5D>h<11>Out:<3A><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hjOhjMhhh&Nh(Nubah}<7D>(h]<5D>h]<5D><>sphx-glr-script-out<75>ah]<5D>h]<5D>h!]<5D>uh%hlh&h'h(K<>hj<>hhubj<62>)<29><>}<7D>(hX<>vector-add-performance:
size Triton Torch
[GH-PAGES] Updated website
2022-07-19 00:52:52 +00:00
0 4096.0 9.600000 9.600000
[GH-PAGES] Updated website
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1 8192.0 19.200000 19.200000
2 16384.0 38.400001 31.999999
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3 32768.0 76.800002 76.800002
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4 65536.0 127.999995 127.999995
5 131072.0 219.428568 219.428568
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6 262144.0 341.333321 384.000001
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7 524288.0 472.615390 472.615390
8 1048576.0 614.400016 614.400016
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9 2097152.0 722.823517 722.823517
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10 4194304.0 780.190482 780.190482
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11 8388608.0 812.429770 812.429770
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12 16777216.0 833.084721 833.084721
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13 33554432.0 842.004273 843.811163
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14 67108864.0 847.448255 848.362445
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15 134217728.0 849.737435 850.656574<EFBFBD>h]<5D>hX<>vector-add-performance:
[GH-PAGES] Updated website
2022-02-09 07:15:50 +00:00
size Triton Torch
[GH-PAGES] Updated website
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0 4096.0 9.600000 9.600000
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1 8192.0 19.200000 19.200000
2 16384.0 38.400001 31.999999
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2022-08-02 00:50:10 +00:00
3 32768.0 76.800002 76.800002
[GH-PAGES] Updated website
2022-02-09 07:15:50 +00:00
4 65536.0 127.999995 127.999995
5 131072.0 219.428568 219.428568
[GH-PAGES] Updated website
2022-08-03 00:52:19 +00:00
6 262144.0 341.333321 384.000001
[GH-PAGES] Updated website
2022-02-09 07:15:50 +00:00
7 524288.0 472.615390 472.615390
8 1048576.0 614.400016 614.400016
[GH-PAGES] Updated website
2022-07-28 00:52:17 +00:00
9 2097152.0 722.823517 722.823517
[GH-PAGES] Updated website
2022-04-23 00:44:46 +00:00
10 4194304.0 780.190482 780.190482
[GH-PAGES] Updated website
2022-02-09 07:15:50 +00:00
11 8388608.0 812.429770 812.429770
[GH-PAGES] Updated website
2022-04-23 00:44:46 +00:00
12 16777216.0 833.084721 833.084721
[GH-PAGES] Updated website
2022-08-03 00:52:19 +00:00
13 33554432.0 842.004273 843.811163
[GH-PAGES] Updated website
2022-05-06 00:44:25 +00:00
14 67108864.0 847.448255 848.362445
[GH-PAGES] Updated website
2022-08-04 00:49:04 +00:00
15 134217728.0 849.737435 850.656574<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhj\ubah}<7D>(h]<5D>h]<5D>jXah]<5D>h]<5D>h!]<5D>h#h$j<><00>j<EFBFBD><00>none<6E>j<EFBFBD>}<7D>uh%j<>h&h'h(K<>hj<>hhubhm)<29><>}<7D>(h<05>B**Total running time of the script:** ( 1 minutes 38.029 seconds)<29>h]<5D>(h <09>strong<6E><67><EFBFBD>)<29><>}<7D>(h<05>%**Total running time of the script:**<2A>h]<5D>h<11>!Total running time of the script:<3A><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhjrubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%jphjlubh<11> ( 1 minutes 38.029 seconds)<29><><EFBFBD><EFBFBD><EFBFBD>}<7D>(h<05> ( 1 minutes 38.029 seconds)<29>hjlhhh&Nh(Nubeh}<7D>(h]<5D>h]<5D><>sphx-glr-timing<6E>ah]<5D>h]<5D>h!]<5D>uh%hlh&h'h(Mhj<>hhubh<62>)<29><>}<7D>(h<05>A.. _sphx_glr_download_getting-started_tutorials_01-vector-add.py:<3A>h]<5D>h}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>hČ<sphx-glr-download-getting-started-tutorials-01-vector-add-py<70>uh%h<>h(Mhj<>hhh&h'ubhb)<29><>}<7D>(hhh]<5D>h <09> container<65><72><EFBFBD>)<29><>}<7D>(hX.. container:: sphx-glr-download sphx-glr-download-python
[GH-PAGES] Updated website
2022-02-09 07:15:50 +00:00
:download:`Download Python source code: 01-vector-add.py <01-vector-add.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
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