Add a mode to fx_codegen_and_compile() to compile in a separate process. This is to prepare for async compile where we'll compile and run eager in parallel (and also be able to move the compile phase to a remote computer).
Added a test based which runs the test_torchinductor tests with subprocess compiling turned on.
Fixed the test which caused the previous version (#146134) to be reverted:
```
$ PYTORCH_TEST_WITH_ROCM=1 PYTORCH_TEST_WITH_SLOW=1 PYTORCH_TEST_SKIP_FAST=1 python test/inductor/test_compile_subprocess.py CpuTests.test_conv_bn_fuse_cpu
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148635
Approved by: https://github.com/jamesjwu
Add a mode to `fx_codegen_and_compile()` to compile in a separate process. This is to prepare for async compile where we'll compile and run eager in parallel (and also be able to move the compile phase to a remote computer).
Added a test based which runs the test_torchinductor tests with subprocess compiling turned on.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146134
Approved by: https://github.com/jamesjwu
This replaces the `__getattr__()` pattern used in remaining OpHandlers with a `DefaultHandler` class defined in part 2.
Some compile time wins from this as well:
```
2025-02-02T19:46:32.2033010Z
2025-02-02T19:46:32.2036607Z WIN: benchmark ('add_loop_inductor', 'compile_time_instruction_count') failed, actual result 29633182927 is -1.71% lower than expected 30150000000 ±1.50% please update the expected results.
2025-02-02T19:46:32.2037575Z
2025-02-02T19:46:32.2037907Z please update all results that changed significantly, and not only the failed ones
2025-02-02T19:46:32.2039291Z PASS: benchmark ('add_loop_inductor_dynamic_gpu', 'compile_time_instruction_count') pass, actual result 43986879172 -1.02% is within expected 44440000000 ±2.50%
2025-02-02T19:46:32.2040131Z
2025-02-02T19:46:32.2041180Z WIN: benchmark ('add_loop_inductor_gpu', 'compile_time_instruction_count') failed, actual result 26246225695 is -1.85% lower than expected 26740000000 ±1.50% please update the expected results.
2025-02-02T19:46:32.2042188Z
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146255
Approved by: https://github.com/shunting314
ghstack dependencies: #146252, #146254
This replaces the `__getattr__()` pattern used in remaining OpHandlers with a `DefaultHandler` class defined in part 2.
Some compile time wins from this as well:
```
2025-02-02T19:46:32.2033010Z
2025-02-02T19:46:32.2036607Z WIN: benchmark ('add_loop_inductor', 'compile_time_instruction_count') failed, actual result 29633182927 is -1.71% lower than expected 30150000000 ±1.50% please update the expected results.
2025-02-02T19:46:32.2037575Z
2025-02-02T19:46:32.2037907Z please update all results that changed significantly, and not only the failed ones
2025-02-02T19:46:32.2039291Z PASS: benchmark ('add_loop_inductor_dynamic_gpu', 'compile_time_instruction_count') pass, actual result 43986879172 -1.02% is within expected 44440000000 ±2.50%
2025-02-02T19:46:32.2040131Z
2025-02-02T19:46:32.2041180Z WIN: benchmark ('add_loop_inductor_gpu', 'compile_time_instruction_count') failed, actual result 26246225695 is -1.85% lower than expected 26740000000 ±1.50% please update the expected results.
2025-02-02T19:46:32.2042188Z
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146255
Approved by: https://github.com/shunting314
ghstack dependencies: #146225, #146226, #146235, #146252, #146254
Summary:
According to angelayi, these two flags indicated different things when we have two-pass codegen but since now we basically keep the two flags all the same, we should merge two flags.
This can prevent some bug (e.g. we change value of aot_mode which will not cover branches like if V.aot_compialtion is True) from happening when we're trying to add different code paths to tweak the value of aot_mode in the future.
Test Plan: CI
Differential Revision: D68122536
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144709
Approved by: https://github.com/angelayi, https://github.com/desertfire
Let me tell you, this was a *journey.*
* When we repropagate through FX interpreter in AOTAutograd, this will reallocate unbacked SymInts. We can eliminate all of these fresh allocations by appropriately asserting equalities on them setting up replacements. See also https://github.com/pytorch/pytorch/issues/111950
* The `inner_fn` of Loops can contain references to unbacked SymInts. We must collect them to prevent DCE.
* Export naughtily accessed `_expr` when it should have accessed `expr` on SymNode. Fixed two sites of this.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117862
Approved by: https://github.com/bdhirsh
This diff introduce the following changes:
1. Fix sympy_subs to preserve integer and non-negative properties of replaced symbol when replacement is string
why is this needed?
I was compiling an expression:
x*abs(y) where y =-2
what happens is that this expression is passed as ``s1*abs(s0)`` then s0 is replaced to ks0 with a call to sympy_subs.
but sympy_subs used to replace s0 (integer=false, nonegative=false) with ks0(inetegr=true, nonegative = true)
resulting in ``x*abs(ks0) = x*ks0`` which is wrong
2. rename sympy_symbol to sympy_index_symbol to make it explicit.
3. add assertion that replaced expression is not passed as string but always a sympy expression.
Fixes https://github.com/pytorch/pytorch/issues/117757
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118150
Approved by: https://github.com/ezyang
Previously it lacked a type hint and so was treated as an Any type. This
resulted in a lot of untyped code downstream as V.graph is referenced in
many places in inductor code. I've typed it properly now as
GraphLowering, and fixed the numerous type errors this surfaced.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114025
Approved by: https://github.com/eellison
ghstack dependencies: #114013
Improves perf of llama_v2 locally from 1.55 -> 1.57
The initial heuristic is to lower to pointwise if # of inputs is <= 4, and all the inputs are pointwise or cannot be memory planned away, or if all the outputs are pointwise.
Perf run was +3% on inference.. There are definitely instances where we should be lowering to foreach_kernels, but it's less flexible for fusion. The motivating example was:
```
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin):
iota = torch.ops.prims.iota.default(512, start = 0, step = 1, dtype = torch.int64, device = device(type='cuda', index=0), requires_grad = False)
# File: /scratch/eellison/work/torchdynamo/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:657, code: position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
unsqueeze = torch.ops.aten.unsqueeze.default(iota, 0)
position_ids = torch.ops.aten.reshape.default(unsqueeze, [-1, 512]); unsqueeze = None
# The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
```
Also not sure if I should be more worried about concatting reduction->pointwise inputs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111233
Approved by: https://github.com/Chillee
