-
Notifications
You must be signed in to change notification settings - Fork 54
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge branch 'main' into user_sched_segmentation_mapping
- Loading branch information
Showing
81 changed files
with
2,107 additions
and
849 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,137 @@ | ||
| # SPDX-FileCopyrightText: Copyright (c) 2024-present NVIDIA CORPORATION & AFFILIATES. | ||
| # All rights reserved. | ||
| # SPDX-License-Identifier: BSD-3-Clause | ||
| import pytest | ||
| from nvfuser import FusionDefinition, DataType | ||
| from .core import run_benchmark | ||
| import torch | ||
|
|
||
|
|
||
| def adaptive_layernorm_fwd_fusion(fd: FusionDefinition, eps: float = 1e-6) -> None: | ||
| T0 = fd.define_tensor( | ||
| shape=[-1, -1, -1], | ||
| contiguity=[True, True, True], | ||
| dtype=DataType.Half, | ||
| is_cpu=False, | ||
| stride_order=[2, 1, 0], | ||
| ) | ||
| T1 = fd.define_tensor( | ||
| shape=[-1, -1], | ||
| contiguity=[True, True], | ||
| dtype=DataType.Half, | ||
| is_cpu=False, | ||
| stride_order=[1, 0], | ||
| ) | ||
| T2 = fd.define_tensor( | ||
| shape=[-1, -1], | ||
| contiguity=[True, True], | ||
| dtype=DataType.Half, | ||
| is_cpu=False, | ||
| stride_order=[1, 0], | ||
| ) | ||
| T3 = fd.ops.cast(T0, dtype=DataType.Float) | ||
| T4, T5 = fd.ops.var_mean(T3, dims=[2], correction=0, keepdim=False) | ||
| T10 = fd.ops.broadcast_in_dim( | ||
| T4, shape=[T0.size(0), T0.size(1), 1], broadcast_dims=[0, 1] | ||
| ) | ||
| T15 = fd.ops.broadcast_in_dim( | ||
| T5, shape=[T0.size(0), T0.size(1), 1], broadcast_dims=[0, 1] | ||
| ) | ||
| S16 = fd.define_scalar(eps, dtype=DataType.Double) | ||
| T17 = fd.ops.add(T10, S16) | ||
| T22 = fd.ops.broadcast_in_dim(T15, shape=T0.shape(), broadcast_dims=[0, 1, 2]) | ||
| T23 = fd.ops.rsqrt(T17) | ||
| T24 = fd.ops.sub(T3, T22) | ||
| T29 = fd.ops.broadcast_in_dim(T23, shape=T0.shape(), broadcast_dims=[0, 1, 2]) | ||
| T30 = fd.ops.mul(T24, T29) | ||
| T35 = fd.ops.reshape(T1, new_shape=[T1.size(0), 1, T1.size(1)]) | ||
| T36 = fd.ops.cast(T35, dtype=DataType.Float) | ||
| S37 = fd.define_scalar(1.00000, dtype=DataType.Double) | ||
| T38 = fd.ops.add(S37, T36) | ||
| T39 = fd.ops.cast(T38, dtype=DataType.Half) | ||
| T44 = fd.ops.broadcast_in_dim(T39, shape=T0.shape(), broadcast_dims=[0, 1, 2]) | ||
| T45 = fd.ops.cast(T44, dtype=DataType.Float) | ||
| T46 = fd.ops.mul(T30, T45) | ||
| T51 = fd.ops.reshape(T2, new_shape=[T2.size(0), 1, T2.size(1)]) | ||
| T56 = fd.ops.broadcast_in_dim(T51, shape=T0.shape(), broadcast_dims=[0, 1, 2]) | ||
| T57 = fd.ops.cast(T56, dtype=DataType.Float) | ||
| T58 = fd.ops.add(T46, T57) | ||
| T59 = fd.ops.cast(T58, dtype=DataType.Half) | ||
| fd.add_output(T5) | ||
| fd.add_output(T23) | ||
| fd.add_output(T59) | ||
|
|
||
|
|
||
| # This benchmark is to particularly track nvFuser host overhead for shape | ||
| # change (dynamic shape support) in the adapative layernorm case. Running a | ||
| # new shape on this fusion without recompiling a new kernel can have significant overhead. | ||
| @pytest.mark.parametrize("host_bench_mode", ["compile", "steady", "dynamic"]) | ||
| def test_adaptive_layernorm_fwd_benchmark( | ||
| benchmark, | ||
| host_bench_mode: str, | ||
| disable_validation: bool, | ||
| disable_benchmarking: bool, | ||
| ): | ||
| B = 1 | ||
| T = 30 * 1024 | ||
| D = 1024 | ||
| inputs = [ | ||
| torch.randn(B, T, D, device="cuda", dtype=torch.float16, requires_grad=True), | ||
| torch.randn(B, D, device="cuda", dtype=torch.float16, requires_grad=True), | ||
| torch.randn(B, D, device="cuda", dtype=torch.float16, requires_grad=True), | ||
| ] | ||
|
|
||
| # Generate multiple inputs to measure dynamic shape overhead. | ||
| if host_bench_mode == "dynamic": | ||
| inputs = [] | ||
| for B in range(1, 3, 1): | ||
| for T in range(30 * 1024, 30 * 1024 + 5 * 128, 128): | ||
| inputs.append( | ||
| [ | ||
| torch.randn( | ||
| B, | ||
| T, | ||
| D, | ||
| device="cuda", | ||
| dtype=torch.float16, | ||
| requires_grad=True, | ||
| ), | ||
| torch.randn( | ||
| B, D, device="cuda", dtype=torch.float16, requires_grad=True | ||
| ), | ||
| torch.randn( | ||
| B, D, device="cuda", dtype=torch.float16, requires_grad=True | ||
| ), | ||
| ] | ||
| ) | ||
|
|
||
| with FusionDefinition() as fd: | ||
| adaptive_layernorm_fwd_fusion(fd) | ||
|
|
||
| def validate(input): | ||
| eps = 1e-6 | ||
| in_tensor, scale, shift = input | ||
| norm_state = torch.nn.LayerNorm(D, elementwise_affine=False, eps=eps) | ||
| norm_out = norm_state(in_tensor) | ||
| mean = in_tensor.to(torch.float).mean(dim=-1) | ||
| variance = in_tensor.to(torch.float).var(dim=-1, unbiased=False) | ||
| invstd = (1.0 / torch.sqrt(variance + eps)).unsqueeze(-1) | ||
| eager_output = norm_out * (1 + scale.view(-1, 1, D)) + shift.view(-1, 1, D) | ||
| fd.validate(input, [mean, invstd, eager_output]) | ||
|
|
||
| if not disable_validation: | ||
| if host_bench_mode == "dynamic": | ||
| # Run validate for all input sizes. | ||
| for input in inputs: | ||
| validate(input) | ||
| else: | ||
| validate(inputs) | ||
|
|
||
| if not disable_benchmarking: | ||
| run_benchmark( | ||
| benchmark, | ||
| None, | ||
| inputs, | ||
| device=f"host:{host_bench_mode}", | ||
| fusion_fn=adaptive_layernorm_fwd_fusion, | ||
| ) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.