feat: data parallel inference examples

docsrc/index.rst

@@ -111,7 +111,13 @@ Tutorials
    tutorials/_rendered_examples/dynamo/torch_compile_transformers_example
    tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage
    tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion
+<<<<<<< HEAD
    tutorials/_rendered_examples/dynamo/custom_kernel_plugins
+=======
+   tutorials/_rendered_examples/distributed_inference/data_parallel_gpt2
+   tutorials/_rendered_examples/distributed_inference/data_parallel_stable_diffusion
+
+>>>>>>> dfbf6ea84 (feat: data parallel inference sample)
 
 Python API Documenation
 ------------------------

examples/distributed_inference/README.md

@@ -0,0 +1,14 @@
+# Torch-TensorRT parallelism for distributed inference
+
+Examples in this folder demonstrates doing distributed inference on multiple devices with Torch-TensorRT backend.
+
+1. Data parallel distributed inference based on [Acclerate](https://huggingface.co/docs/accelerate/usage_guides/distributed_inference)
+
+Using Accelerate users can achieve data parallel distributed inference with Torch-TensorRt backend. In this case, the entire model
+will be loaded onto each GPU and different chunks of batch input is processed on each device.
+
+See the examples started with `data_parallel` for more details.
+
+2. Tensor parallel distributed inference
+
+In development.

examples/distributed_inference/data_parallel_gpt2.py

@@ -0,0 +1,64 @@
+"""
+.. _data_parallel_gpt2:
+
+Torch-TensorRT Distributed Inference
+======================================================
+
+This interactive script is intended as a sample of distributed inference using data
+parallelism using Accelerate
+library with the Torch-TensorRT workflow on GPT2 model.
+
+"""
+
+# %%
+# Imports and Model Definition
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+import torch
+from accelerate import PartialState
+from transformers import AutoTokenizer, GPT2LMHeadModel
+
+import torch_tensorrt
+
+tokenizer = AutoTokenizer.from_pretrained("gpt2")
+
+# Set input prompts for different devices
+prompt1 = "GPT2 is a model developed by."
+prompt2 = "Llama is a model developed by "
+
+input_id1 = tokenizer(prompt1, return_tensors="pt").input_ids
+input_id2 = tokenizer(prompt2, return_tensors="pt").input_ids
+
+distributed_state = PartialState()
+
+# Import GPT2 model and load to distributed devices
+model = GPT2LMHeadModel.from_pretrained("gpt2").eval().to(distributed_state.device)
+
+
+# Instantiate model with Torch-TensorRT backend
+model.forward = torch.compile(
+    model.forward,
+    backend="torch_tensorrt",
+    options={
+        "truncate_long_and_double": True,
+        "enabled_precisions": {torch.float16},
+        "debug": True,
+    },
+    dynamic=False,
+)
+
+# %%
+# Inference
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+# Assume there are 2 processes (2 devices)
+with distributed_state.split_between_processes([input_id1, input_id2]) as prompt:
+    cur_input = torch.clone(prompt[0]).to(distributed_state.device)
+
+    gen_tokens = model.generate(
+        cur_input,
+        do_sample=True,
+        temperature=0.9,
+        max_length=100,
+    )
+    gen_text = tokenizer.batch_decode(gen_tokens)[0]

examples/distributed_inference/data_parallel_stable_diffusion.py

@@ -0,0 +1,61 @@
+"""
+.. _data_parallel_stable_diffusion:
+
+Torch-TensorRT Distributed Inference
+======================================================
+
+This interactive script is intended as a sample of distributed inference using data
+parallelism using Accelerate
+library with the Torch-TensorRT workflow on Stable Diffusion model.
+
+"""
+
+# %%
+# Imports and Model Definition
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+import torch
+from accelerate import PartialState
+from diffusers import DiffusionPipeline
+
+import torch_tensorrt
+
+model_id = "CompVis/stable-diffusion-v1-4"
+
+# Instantiate Stable Diffusion Pipeline with FP16 weights
+pipe = DiffusionPipeline.from_pretrained(
+    model_id, revision="fp16", torch_dtype=torch.float16
+)
+
+distributed_state = PartialState()
+pipe = pipe.to(distributed_state.device)
+
+backend = "torch_tensorrt"
+
+# Optimize the UNet portion with Torch-TensorRT
+pipe.unet = torch.compile(  # %%
+    # Inference
+    # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    # Assume there are 2 processes (2 devices)
+    pipe.unet,
+    backend=backend,
+    options={
+        "truncate_long_and_double": True,
+        "precision": torch.float16,
+        "debug": True,
+        "use_python_runtime": True,
+    },
+    dynamic=False,
+)
+torch_tensorrt.runtime.set_multi_device_safe_mode(True)
+
+
+# %%
+# Inference
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+# Assume there are 2 processes (2 devices)
+with distributed_state.split_between_processes(["a dog", "a cat"]) as prompt:
+    print("before \n")
+    result = pipe(prompt).images[0]
+    print("after ")
+    result.save(f"result_{distributed_state.process_index}.png")

examples/distributed_inference/requirement.txt

@@ -0,0 +1,3 @@
+accelerate
+transformers
+diffusers