[halide-backend] Initial implementation of HalideKernel and HalideScheduling #126417
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This was referenced May 16, 2024
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/126417
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 20c7437 with merge base bc8883a ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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ghstack-source-id: 227c314f43e869d8863814d83cb7dbc077b2783e Pull Request resolved: #126417
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ghstack-source-id: 77b1d63cd88e04b737fe9ff5652a882becdca2f6 Pull Request resolved: #126417
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ghstack-source-id: 6934e15117b649c40cd104248ebb299360da0f0b Pull Request resolved: #126417
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ghstack-source-id: af65a4e9b1c52966233a70892195ba5c6d4f3402 Pull Request resolved: #126417
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ghstack-source-id: 7290fcc2d09170299f3dab83a6491e4318f6ccca Pull Request resolved: pytorch#126417
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This puts the halide runtime in a global shared object, rather than copying it to each kernel. Having many copies of the runtime causes many issues with cuda. Pull Request resolved: #129025 Approved by: https://github.com/shunting314, https://github.com/eellison ghstack dependencies: #126417
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@pytorchbot revert -m "breaking internal builds" -c ghfirst |
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…alideScheduling (#126417)" This reverts commit 4f9399b. Reverted #126417 on behalf of https://github.com/fbgheith due to breaking internal builds ([comment](#126417 (comment)))
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This was referenced Jun 25, 2024
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This puts the halide runtime in a global shared object, rather than copying it to each kernel. Having many copies of the runtime causes many issues with cuda. Pull Request resolved: #129025 Approved by: https://github.com/shunting314, https://github.com/eellison ghstack dependencies: #126417
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Prior to this the generated Halide code was a rather literal translation of the Triton code, with XBLOCK/YBLOCK/RBLOCK and 1D inputs. Halide prefers dimensions, and this 1D index triggers a lot of bugs and perf issues. This PR infers dimensions and changes the indexing in the generated code.
Before
```py
@hl.generator(name="kernel")
class Kernel:
in_ptr0 = hl.InputBuffer(hl.Float(32), 1)
out_ptr3 = hl.OutputBuffer(hl.Float(32), 2)
def generate(g):
in_ptr0 = g.in_ptr0
out_ptr3 = g.out_ptr3
xindex = hl.Var('xindex')
rindex = hl.Var('rindex')
r1 = rindex
x0 = xindex
idom = hl.RDom([hl.Range(0, 16), hl.Range(0, 32)])
odom = hl.RDom([hl.Range(0, 16)])
rdom = hl.RDom([hl.Range(0, 32)])
xindex_idom = idom.x
xindex_odom = odom.x
rindex_idom = idom.y
r1_idom = rindex_idom
x0_idom = xindex_idom
x0_odom = xindex_odom
tmp0 = hl.Func('tmp0')
tmp0[rindex, xindex] = in_ptr0[r1 + (32*x0)]
tmp1 = hl.Func('tmp1')
tmp1[xindex] = hl.maximum(rdom, tmp0[rdom, xindex])
tmp2 = hl.Func('tmp2')
tmp2[rindex, xindex] = tmp0[rindex, xindex] - tmp1[xindex]
tmp3 = hl.Func('tmp3')
tmp3[rindex, xindex] = hl.fast_exp(hl.cast(hl.Float(32), tmp2[rindex, xindex])) if tmp2.type().bits() <= 32 else hl.exp(tmp2[rindex, xindex])
tmp4 = hl.Func('tmp4')
tmp4[xindex] = hl.sum(rdom, tmp3[rdom, xindex])
tmp5 = hl.Func('tmp5')
tmp5[rindex, xindex] = tmp3[rindex, xindex] / tmp4[xindex]
out_ptr3_i0 = hl.Var('out_ptr3_i0')
out_ptr3_i1 = hl.Var('out_ptr3_i1')
out_ptr3[out_ptr3_i0, out_ptr3_i1] = hl.cast(out_ptr3.type(), tmp5[out_ptr3_i0, out_ptr3_i1])
assert g.using_autoscheduler()
in_ptr0.set_estimates([hl.Range(0, 512)])
out_ptr3.set_estimates([hl.Range(0, 32), hl.Range(0, 16)])
```
After
```py
@hl.generator(name="kernel")
class Kernel:
in_ptr0 = hl.InputBuffer(hl.Float(32), 2)
out_ptr3 = hl.OutputBuffer(hl.Float(32), 2)
def generate(g):
in_ptr0 = g.in_ptr0
out_ptr3 = g.out_ptr3
h0 = hl.Var('h0')
h1 = hl.Var('h1')
rdom = hl.RDom([hl.Range(0, 32)])
hr1 = rdom[0]
tmp0 = hl.Func('tmp0')
tmp0[h0, h1] = in_ptr0[h0, h1,]
tmp1 = hl.Func('tmp1')
tmp1[h1] = hl.maximum(rdom, tmp0[hr1, h1])
tmp2 = hl.Func('tmp2')
tmp2[h0, h1] = tmp0[h0, h1] - tmp1[h1]
tmp3 = hl.Func('tmp3')
tmp3[h0, h1] = hl.fast_exp(hl.cast(hl.Float(32), tmp2[h0, h1])) if tmp2.type().bits() <= 32 else hl.exp(tmp2[h0, h1])
tmp4 = hl.Func('tmp4')
tmp4[h1] = hl.sum(rdom, tmp3[hr1, h1])
tmp5 = hl.Func('tmp5')
tmp5[h0, h1] = tmp3[h0, h1] / tmp4[h1]
out_ptr3[h0, h1,] = hl.cast(hl.Float(32), tmp5[h0, h1])
assert g.using_autoscheduler()
in_ptr0.dim(0).set_min(0)
in_ptr0.dim(0).set_stride(1)
in_ptr0.dim(0).set_extent(32)
in_ptr0.dim(1).set_min(0)
in_ptr0.dim(1).set_stride(32)
in_ptr0.dim(1).set_extent(16)
in_ptr0.set_estimates([hl.Range(0, 32), hl.Range(0, 16)])
out_ptr3.set_estimates([hl.Range(0, 32), hl.Range(0, 16)])
```
Pull Request resolved: #129026
Approved by: https://github.com/shunting314, https://github.com/eellison
ghstack dependencies: #126417, #129025
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Pull Request resolved: #127506 Approved by: https://github.com/shunting314, https://github.com/eellison ghstack dependencies: #126417, #129025, #129026
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Requires halide/Halide#8255 Pull Request resolved: #129036 Approved by: https://github.com/shunting314, https://github.com/eellison ghstack dependencies: #126417, #129025, #129026, #127506
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In theory Halide doesn't need the split reduction stuff we do for Triton since it can generate multiple kernels. Pull Request resolved: #129320 Approved by: https://github.com/shunting314, https://github.com/eellison ghstack dependencies: #126417, #129025, #129026, #127506, #129036
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