Optimize Stable Diffusion (#371)
* initial commit * make UNet stream capturable * try to fix noise_pred value * remove cuda graph and keep NB * non blocking unet with PNDMScheduler * make timesteps np arrays for pndm scheduler because lists don't get formatted to tensors in `self.set_format` * make max async in pndm * use channel last format in unet * avoid moving timesteps device in each unet call * avoid memcpy op in `get_timestep_embedding` * add `channels_last` kwarg to `DiffusionPipeline.from_pretrained` * update TODO * replace `channels_last` kwarg with `memory_format` for more generality * revert the channels_last changes to leave it for another PR * remove non_blocking when moving input ids to device * remove blocking from all .to() operations at beginning of pipeline * fix merging * fix merging * model can run in other precisions without autocast * attn refactoring * Revert "attn refactoring" This reverts commit 0c70c0e189cd2c4d8768274c9fcf5b940ee310fb. * remove restriction to run conv_norm in fp32 * use `baddbmm` instead of `matmul`for better in attention for better perf * removing all reshapes to test perf * Revert "removing all reshapes to test perf" This reverts commit 006ccb8a8c6bc7eb7e512392e692a29d9b1553cd. * add shapes comments * hardcore whats needed for jitting * Revert "hardcore whats needed for jitting" This reverts commit 2fa9c698eae2890ac5f8e367ca80532ecf94df9a. * Revert "remove restriction to run conv_norm in fp32" This reverts commit cec592890c32da3d1b78d38b49e4307aedf459b9. * revert using baddmm in attention's forward * cleanup comment * remove restriction to run conv_norm in fp32. no quality loss was noticed This reverts commit cc9bc1339c998ebe9e7d733f910c6d72d9792213. * add more optimizations techniques to docs * Revert "add shapes comments" This reverts commit 31c58eadb8892f95478cdf05229adf678678c5f4. * apply suggestions * make quality * apply suggestions * styling * `scheduler.timesteps` are now arrays so we dont need .to() * remove useless .type() * use mean instead of max in `test_stable_diffusion_inpaint_pipeline_k_lms` * move scheduler timestamps to correct device if tensors * add device to `set_timesteps` in LMSD scheduler * `self.scheduler.set_timesteps` now uses device arg for schedulers that accept it * quick fix * styling * remove kwargs from schedulers `set_timesteps` * revert to using max in K-LMS inpaint pipeline test * Revert "`self.scheduler.set_timesteps` now uses device arg for schedulers that accept it" This reverts commit 00d5a51e5c20d8d445c8664407ef29608106d899. * move timesteps to correct device before loop in SD pipeline * apply previous fix to other SD pipelines * UNet now accepts tensor timesteps even on wrong device, to avoid errors - it shouldnt affect performance if timesteps are alrdy on correct device - it does slow down performance if they're on the wrong device * fix pipeline when timesteps are arrays with strides
This commit is contained in:
Nouamane Tazi
GitHub
parent
a7058f42e1
commit
9ebaea545f
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@ -14,7 +14,64 @@ specific language governing permissions and limitations under the License.
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We present some techniques and ideas to optimize 🤗 Diffusers _inference_ for memory or speed.
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## CUDA `autocast`
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<table>
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<tr>
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<td>
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<td>Latency
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<td>Speedup
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<tr>
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<tr>
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<td>original
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<td>9.50s
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<td>x1
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<tr>
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<tr>
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<td>cuDNN auto-tuner
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<td>9.37s
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<td>x1.01
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<tr>
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<td>autocast (fp16)
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<td>5.47s
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<td>x1.91
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<tr>
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<td>fp16
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<td>3.61s
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<td>x2.91
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<tr>
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<td>channels last
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<td>3.30s
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<td>x2.87
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<tr>
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<tr>
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<td>traced UNet
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<td>3.21s
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<td>x2.96
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</table>
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<em>obtained on NVIDIA TITAN RTX by generating a single image of size 512x512 from the prompt "a photo of an astronaut riding a horse on mars" with 50 DDIM steps.</em>
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## Enable cuDNN auto-tuner
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[NVIDIA cuDNN](https://developer.nvidia.com/cudnn) supports many algorithms to compute a convolution. Autotuner runs a short benchmark and selects the kernel with the best performance on a given hardware for a given input size.
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Since we’re using **convolutional networks** (other types currently not supported), we can enable cuDNN autotuner before launching the inference by setting:
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```python
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import torch
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torch.backends.cudnn.benchmark = True
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```
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### Use tf32 instead of fp32 (on Ampere and later CUDA devices)
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On Ampere and later CUDA devices matrix multiplications and convolutions can use the TensorFloat32 (TF32) mode for faster but slightly less accurate computations. By default PyTorch enables TF32 mode for convolutions but not matrix multiplications, and unless a network requires full float32 precision we recommend enabling this setting for matrix multiplications, too. It can significantly speed up computations with typically negligible loss of numerical accuracy. You can read more about it [here](https://huggingface.co/docs/transformers/v4.18.0/en/performance#tf32). All you need to do is to add this before your inference:
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```python
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import torch
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torch.backends.cuda.matmul.allow_tf32 = True
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```
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## Automatic mixed precision (AMP)
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If you use a CUDA GPU, you can take advantage of `torch.autocast` to perform inference roughly twice as fast at the cost of slightly lower precision. All you need to do is put your inference call inside an `autocast` context manager. The following example shows how to do it using Stable Diffusion text-to-image generation as an example:
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@ -47,7 +104,7 @@ pipe = StableDiffusionPipeline.from_pretrained(
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## Sliced attention for additional memory savings
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For even additional memory savings, you can use a sliced version of attention that performs the computation in steps instead of all at once.
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For even additional memory savings, you can use a sliced version of attention that performs the computation in steps instead of all at once.
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<Tip>
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Attention slicing is useful even if a batch size of just 1 is used - as long as the model uses more than one attention head. If there is more than one attention head the *QK^T* attention matrix can be computed sequentially for each head which can save a significant amount of memory.
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@ -73,4 +130,139 @@ with torch.autocast("cuda"):
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image = pipe(prompt).images[0]
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```
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There's a small performance penalty of about 10% slower inference times, but this method allows you to use Stable Diffusion in as little as 3.2 GB of VRAM!
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There's a small performance penalty of about 10% slower inference times, but this method allows you to use Stable Diffusion in as little as 3.2 GB of VRAM!
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## Using Channels Last memory format
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Channels last memory format is an alternative way of ordering NCHW tensors in memory preserving dimensions ordering. Channels last tensors ordered in such a way that channels become the densest dimension (aka storing images pixel-per-pixel). Since not all operators currently support channels last format it may result in a worst performance, so it's better to try it and see if it works for your model.
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For example, in order to set the UNet model in our pipeline to use channels last format, we can use the following:
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```python
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print(pipe.unet.conv_out.state_dict()["weight"].stride()) # (2880, 9, 3, 1)
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pipe.unet.to(memory_format=torch.channels_last) # in-place operation
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print(
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pipe.unet.conv_out.state_dict()["weight"].stride()
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) # (2880, 1, 960, 320) haveing a stride of 1 for the 2nd dimension proves that it works
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```
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## Tracing
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Tracing runs an example input tensor through your model, and captures the operations that are invoked as that input makes its way through the model's layers so that an executable or `ScriptFunction` is returned that will be optimized using just-in-time compilation.
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To trace our UNet model, we can use the following:
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```python
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import time
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import torch
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from diffusers import StableDiffusionPipeline
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import functools
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# torch disable grad
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torch.set_grad_enabled(False)
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# set variables
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n_experiments = 2
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unet_runs_per_experiment = 50
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# load inputs
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def generate_inputs():
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sample = torch.randn(2, 4, 64, 64).half().cuda()
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timestep = torch.rand(1).half().cuda() * 999
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encoder_hidden_states = torch.randn(2, 77, 768).half().cuda()
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return sample, timestep, encoder_hidden_states
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pipe = StableDiffusionPipeline.from_pretrained(
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"CompVis/stable-diffusion-v1-4",
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# scheduler=scheduler,
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use_auth_token=True,
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revision="fp16",
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torch_dtype=torch.float16,
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).to("cuda")
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unet = pipe.unet
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unet.eval()
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unet.to(memory_format=torch.channels_last) # use channels_last memory format
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unet.forward = functools.partial(unet.forward, return_dict=False) # set return_dict=False as default
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# warmup
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for _ in range(3):
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with torch.inference_mode():
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inputs = generate_inputs()
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orig_output = unet(*inputs)
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# trace
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print("tracing..")
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unet_traced = torch.jit.trace(unet, inputs)
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unet_traced.eval()
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print("done tracing")
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# warmup and optimize graph
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for _ in range(5):
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with torch.inference_mode():
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inputs = generate_inputs()
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orig_output = unet_traced(*inputs)
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# benchmarking
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with torch.inference_mode():
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for _ in range(n_experiments):
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torch.cuda.synchronize()
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start_time = time.time()
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for _ in range(unet_runs_per_experiment):
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orig_output = unet_traced(*inputs)
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torch.cuda.synchronize()
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print(f"unet traced inference took {time.time() - start_time:.2f} seconds")
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for _ in range(n_experiments):
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torch.cuda.synchronize()
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start_time = time.time()
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for _ in range(unet_runs_per_experiment):
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orig_output = unet(*inputs)
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torch.cuda.synchronize()
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print(f"unet inference took {time.time() - start_time:.2f} seconds")
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# save the model
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unet_traced.save("unet_traced.pt")
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```
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Then we can replace the `unet` attribute of the pipeline with the traced model like the following
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```python
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from diffusers import StableDiffusionPipeline
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import torch
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from dataclasses import dataclass
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@dataclass
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class UNet2DConditionOutput:
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sample: torch.FloatTensor
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pipe = StableDiffusionPipeline.from_pretrained(
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"CompVis/stable-diffusion-v1-4",
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# scheduler=scheduler,
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use_auth_token=True,
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revision="fp16",
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torch_dtype=torch.float16,
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).to("cuda")
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# use jitted unet
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unet_traced = torch.jit.load("unet_traced.pt")
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# del pipe.unet
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class TracedUNet(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.in_channels = pipe.unet.in_channels
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self.device = pipe.unet.device
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def forward(self, latent_model_input, t, encoder_hidden_states):
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sample = unet_traced(latent_model_input, t, encoder_hidden_states)[0]
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return UNet2DConditionOutput(sample=sample)
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pipe.unet = TracedUNet()
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with torch.inference_mode():
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image = pipe([prompt] * 1, num_inference_steps=50).images[0]
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```
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@ -72,8 +72,7 @@ class AttentionBlock(nn.Module):
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# get scores
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scale = 1 / math.sqrt(math.sqrt(self.channels / self.num_heads))
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attention_scores = torch.matmul(query_states * scale, key_states.transpose(-1, -2) * scale)
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attention_scores = torch.matmul(query_states * scale, key_states.transpose(-1, -2) * scale) # TODO: use baddmm
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attention_probs = torch.softmax(attention_scores.float(), dim=-1).type(attention_scores.dtype)
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# compute attention output
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@ -275,7 +274,13 @@ class CrossAttention(nn.Module):
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return self.to_out(hidden_states)
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def _attention(self, query, key, value):
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attention_scores = torch.matmul(query, key.transpose(-1, -2)) * self.scale
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attention_scores = torch.baddbmm(
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torch.empty(query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device),
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query,
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key.transpose(-1, -2),
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beta=0,
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alpha=self.scale,
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)
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attention_probs = attention_scores.softmax(dim=-1)
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# compute attention output
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hidden_states = torch.matmul(attention_probs, value)
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@ -292,7 +297,9 @@ class CrossAttention(nn.Module):
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for i in range(hidden_states.shape[0] // slice_size):
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start_idx = i * slice_size
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end_idx = (i + 1) * slice_size
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attn_slice = torch.matmul(query[start_idx:end_idx], key[start_idx:end_idx].transpose(1, 2)) * self.scale
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attn_slice = (
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torch.matmul(query[start_idx:end_idx], key[start_idx:end_idx].transpose(1, 2)) * self.scale
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) # TODO: use baddbmm for better performance
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attn_slice = attn_slice.softmax(dim=-1)
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attn_slice = torch.matmul(attn_slice, value[start_idx:end_idx])
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@ -37,10 +37,12 @@ def get_timestep_embedding(
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assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
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half_dim = embedding_dim // 2
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exponent = -math.log(max_period) * torch.arange(start=0, end=half_dim, dtype=torch.float32)
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exponent = -math.log(max_period) * torch.arange(
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start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
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)
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exponent = exponent / (half_dim - downscale_freq_shift)
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emb = torch.exp(exponent).to(device=timesteps.device)
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emb = torch.exp(exponent)
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emb = timesteps[:, None].float() * emb[None, :]
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# scale embeddings
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@ -331,7 +331,7 @@ class ResnetBlock2D(nn.Module):
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# make sure hidden states is in float32
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# when running in half-precision
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hidden_states = self.norm1(hidden_states).type(hidden_states.dtype)
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hidden_states = self.norm1(hidden_states)
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hidden_states = self.nonlinearity(hidden_states)
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if self.upsample is not None:
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@ -349,7 +349,7 @@ class ResnetBlock2D(nn.Module):
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# make sure hidden states is in float32
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# when running in half-precision
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hidden_states = self.norm2(hidden_states).type(hidden_states.dtype)
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hidden_states = self.norm2(hidden_states)
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hidden_states = self.nonlinearity(hidden_states)
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hidden_states = self.dropout(hidden_states)
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@ -230,16 +230,16 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin):
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# 1. time
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timesteps = timestep
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if not torch.is_tensor(timesteps):
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# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
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timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)
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elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
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timesteps = timesteps.to(dtype=torch.float32)
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timesteps = timesteps[None].to(device=sample.device)
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timesteps = timesteps[None].to(sample.device)
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# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
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timesteps = timesteps.expand(sample.shape[0])
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t_emb = self.time_proj(timesteps)
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emb = self.time_embedding(t_emb)
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emb = self.time_embedding(t_emb.to(self.dtype))
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# 2. pre-process
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sample = self.conv_in(sample)
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# 6. post-process
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# make sure hidden states is in float32
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# when running in half-precision
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sample = self.conv_norm_out(sample.float()).type(sample.dtype)
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sample = self.conv_norm_out(sample)
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sample = self.conv_act(sample)
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sample = self.conv_out(sample)
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@ -225,15 +225,23 @@ class StableDiffusionPipeline(DiffusionPipeline):
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latents_shape,
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generator=generator,
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device=latents_device,
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dtype=text_embeddings.dtype,
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)
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else:
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if latents.shape != latents_shape:
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raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
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latents = latents.to(self.device)
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latents = latents.to(latents_device)
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# set timesteps
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self.scheduler.set_timesteps(num_inference_steps)
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# Some schedulers like PNDM have timesteps as arrays
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# It's more optimzed to move all timesteps to correct device beforehand
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if torch.is_tensor(self.scheduler.timesteps):
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timesteps_tensor = self.scheduler.timesteps.to(self.device)
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else:
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timesteps_tensor = torch.tensor(self.scheduler.timesteps.copy(), device=self.device)
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# if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
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if isinstance(self.scheduler, LMSDiscreteScheduler):
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latents = latents * self.scheduler.sigmas[0]
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@ -247,7 +255,7 @@ class StableDiffusionPipeline(DiffusionPipeline):
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if accepts_eta:
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extra_step_kwargs["eta"] = eta
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for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
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for i, t in enumerate(self.progress_bar(timesteps_tensor)):
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# expand the latents if we are doing classifier free guidance
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latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
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if isinstance(self.scheduler, LMSDiscreteScheduler):
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@ -278,7 +286,9 @@ class StableDiffusionPipeline(DiffusionPipeline):
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# run safety checker
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safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
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image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_checker_input.pixel_values)
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image, has_nsfw_concept = self.safety_checker(
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images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
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)
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if output_type == "pil":
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image = self.numpy_to_pil(image)
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@ -265,7 +265,11 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
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latents = init_latents
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t_start = max(num_inference_steps - init_timestep + offset, 0)
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for i, t in enumerate(self.progress_bar(self.scheduler.timesteps[t_start:])):
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# Some schedulers like PNDM have timesteps as arrays
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# It's more optimzed to move all timesteps to correct device beforehand
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timesteps_tensor = torch.tensor(self.scheduler.timesteps[t_start:], device=self.device)
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for i, t in enumerate(self.progress_bar(timesteps_tensor)):
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t_index = t_start + i
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# expand the latents if we are doing classifier free guidance
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@ -298,7 +298,11 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
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latents = init_latents
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t_start = max(num_inference_steps - init_timestep + offset, 0)
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for i, t in tqdm(enumerate(self.scheduler.timesteps[t_start:])):
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# Some schedulers like PNDM have timesteps as arrays
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# It's more optimzed to move all timesteps to correct device beforehand
|
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timesteps_tensor = torch.tensor(self.scheduler.timesteps[t_start:], device=self.device)
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for i, t in tqdm(enumerate(timesteps_tensor)):
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t_index = t_start + i
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||||
# expand the latents if we are doing classifier free guidance
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latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
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|
|
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@ -131,13 +131,15 @@ class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
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||||
return integrated_coeff
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||||
def set_timesteps(self, num_inference_steps: int):
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||||
def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
|
||||
"""
|
||||
Sets the timesteps used for the diffusion chain. Supporting function to be run before inference.
|
||||
|
||||
Args:
|
||||
num_inference_steps (`int`):
|
||||
the number of diffusion steps used when generating samples with a pre-trained model.
|
||||
device (`str` or `torch.device`, optional):
|
||||
the device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
|
||||
"""
|
||||
self.num_inference_steps = num_inference_steps
|
||||
|
||||
|
|
@ -145,8 +147,8 @@ class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
|
|||
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
|
||||
sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
|
||||
sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32)
|
||||
self.sigmas = torch.from_numpy(sigmas)
|
||||
self.timesteps = torch.from_numpy(timesteps)
|
||||
self.sigmas = torch.from_numpy(sigmas).to(device=device)
|
||||
self.timesteps = torch.from_numpy(timesteps).to(device=device)
|
||||
|
||||
self.derivatives = []
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue