Add option to use unbacked, and backed size obl dynamic shapes for more sounds compilation.

docs/design/debug_vllm_compile.md

@@ -151,6 +151,76 @@ To avoid this, please either:
 2. wrap the branching logic into a custom operator. TorchDynamo does not
 trace into custom operators.
 
+## Debugging constraint violations and dynamic shapes guards issues
+
+Dynamic-shape guards are a specific category of Dynamo guards. They are constraints that `torch.compile`
+attaches to dynamic dimensions (e.g., `seq_len`) to ensure the compiled artifact remains valid.
+These guards typically appear when framework code, custom passes, or user code branches based on
+dynamic shape values.
+
+**Example:**
+
+```python
+if x > 10:
+    # path A
+else:
+    # path B
+```
+
+This creates a guard `x > 10` or `x <= 10` depending on which path was traced.
+
+**vLLM's Assumption:**
+vLLM assumes that all guards added by torch.compile are safe to drop and will not
+constrain the compiled graph to specific input shapes. When this assumption is violated,
+it can cause issues that users need to debug.
+Some side effects that indicates this assumption is violated are runtime errors
+or `ConstraintViolationErrors`.
+
+A `ConstraintViolationErrors` will be thrown if a dynamic shape gets constrained to
+a single value. If you encounter a constraint violation error or suspect that a dynamic
+shapes guard is being added incorrectly, you can use stricter dynamic shape modes to
+help debug the issue:
+
+```sh
+# Online - using unbacked mode
+vllm serve meta-llama/Llama-3.2-1B -O.dynamic_shapes_config.type=unbacked
+
+# Online - using backed_size_oblivious mode
+vllm serve meta-llama/Llama-3.2-1B -O.dynamic_shapes_config.type=backed_size_oblivious
+```
+
+```py
+# Offline - using unbacked mode
+from vllm.config.compilation import CompilationConfig, DynamicShapesConfig, DynamicShapesType
+LLM(model, compilation_config=CompilationConfig(
+    dynamic_shapes_config=DynamicShapesConfig(type=DynamicShapesType.UNBACKED)
+))
+
+# Offline - using backed_size_oblivious mode
+from vllm.config.compilation import CompilationConfig, DynamicShapesConfig, DynamicShapesType
+LLM(model, compilation_config=CompilationConfig(
+    dynamic_shapes_config=DynamicShapesConfig(type=DynamicShapesType.BACKED_SIZE_OBLIVIOUS)
+))
+```
+
+These modes are stricter and reduce or eliminate the need of dynamic shapes guarding, which can help isolate issues:
+
+- `unbacked`: Uses unbacked symints which don't allow guards, making it easier to identify where guards are being incorrectly added
+- `backed_size_oblivious`: Uses a mode that is more strict about guarding.
+
+For more details on dynamic shapes modes, see [Dynamic shapes and vLLM guard dropping](torch_compile.md#dynamic-shapes-and-vllm-guard-dropping).
+
+### Printing guards
+
+To see all guards that are being added during compilation, you can use `TORCH_LOGS=+dynamic`:
+
+```sh
+TORCH_LOGS=+dynamic vllm serve meta-llama/Llama-3.2-1B
+```
+
+Look for `[guard added]` in the logs to see where guards are being added. This can help you identify which operations are
+causing guards to be added incorrectly.
+
 ## Debugging TorchInductor
 
 TorchInductor takes a captured graph and then compiles it down to some Python code

docs/design/torch_compile.md

@@ -29,6 +29,109 @@ A unique aspect of vLLM's `torch.compile` integration, is that we guarantee all
 
 By default, the cache saves compiled artifacts as binary files. If you would like to interact with the generated code for debugging purposes, set the field `compile_cache_save_format=unpacked` in the compilation config, or omit this and set the env variable `VLLM_COMPILE_CACHE_SAVE_FORMAT=unpacked`.
 
+## Dynamic shapes and vllm guard dropping
+
+`torch.compile` is designed to guard on dynamic shapes with no hesitation
+when needed. This contradicts with vLLM's `torch.compile` approach of
+dropping the guards since many of those guards could be material.
+
+`torch.compile` provides two kinds of dynamic shapes: `backed` and `unbacked`.
+`torch.compile` guards on `backed` dynamic shapes and does not provide a
+guarantee that no guards will be added to them. User code, dynamo,
+inductor, and autograd all can add guards. Moreover, for 0/1
+specializations, backed symbols are specialized unconditionally to 0, 1,
+or >=2 even without encountering a branching on those ranges.
+
+On the contrary, `unbacked` dynamic shapes are guaranteed not to be guarded
+on and are not 0/1 specialized. However, there is a possibility of
+throwing a data dependent error when a branch that requires their value is
+encountered and no explicit unbacked handling is defined. The framework is
+converging to a state where it won't throw DDE but rather pick general
+paths. One downside of using unbacked is missed optimization opportunities
+due to either perf bugs or picking general paths, also using a fixed
+non-example input-based hint (this will be fixed soon with override_hint
+API). An example of picking general paths is assuming input not contiguous
+in functions call contiguous() and reshape() when can't be symbolically proven
+with a change of introducing a clone.
+
+`backed_size_oblivious` is a flag that enables treating backed symbols as
+unbacked wherever explicit handling for unbacked is defined. With this
+mode, 0/1 specializations are mostly avoided in framework code and the
+default 0/1 specialization does not happen. However, there is still no
+guarantee that torch.compile won't guard, especially due to user code or
+custom passes. `backed_size_oblivious` is experimental in PyTorch compile
+and could be deprecated. That said, it's a safer option to use than
+`backed` and the probability of reducing performance is lower than
+`unbacked`.
+
+### Configuring Dynamic Shapes
+
+The `DynamicShapesConfig` allows you to control the dynamic shapes behavior by
+setting the `type` field. You can choose between three modes:
+`BACKED`(default), `UNBACKED` , and `BACKED_SIZE_OBLIVIOUS`.
+
+#### Offline Inference Example (Using LLM class)
+
+When using the `LLM` class for offline inference, you can configure dynamic
+shapes through the `compilation_config` parameter:
+
+```python
+from vllm import LLM, SamplingParams
+from vllm.config.compilation import CompilationConfig, DynamicShapesConfig, DynamicShapesType
+
+# Example: Using backed_size_oblivious (experimental, safer than backed)
+llm = LLM(
+    model="meta-llama/Llama-3.2-1B",
+    compilation_config=CompilationConfig(
+        dynamic_shapes_config=DynamicShapesConfig(
+            type=DynamicShapesType.BACKED_SIZE_OBLIVIOUS
+        )
+    )
+)
+
+# Example: Using unbacked (strongest guarantee against guards)
+llm = LLM(
+    model="meta-llama/Llama-3.2-1B",
+    compilation_config=CompilationConfig(
+        dynamic_shapes_config=DynamicShapesConfig(
+            type=DynamicShapesType.UNBACKED
+        )
+    )
+)
+
+# Generate outputs
+prompts = ["Hello, my name is", "The future of AI is"]
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+outputs = llm.generate(prompts, sampling_params)
+```
+
+#### Online Serving Example (Using vllm serve)
+
+When using `vllm serve` for online serving, you can configure dynamic shapes
+through the `--compilation-config` flag:
+
+```bash
+# Example: Using unbacked
+vllm serve meta-llama/Llama-3.2-1B \
+  --compilation-config '{"dynamic_shapes_config": {"type": "unbacked"}}'
+
+
+# Alternative: Using dot notation (simpler for single values)
+vllm serve meta-llama/Llama-3.2-1B -O.dynamic_shapes_config.type=unbacked
+```
+
+#### Choosing the Right Mode
+
+- **BACKED** (default): Use when you're willing to accept potential unsafe dropping of guards
+for maximal performance. Guard could be unsoundly added and then ignored.
+
+- **UNBACKED**  Use when you need the strongest guarantee against guards.
+  This is the most conservative option but may miss some optimization opportunities.
+
+- **BACKED_SIZE_OBLIVIOUS**: Use when you want a balance between avoiding guards
+  and performance. This experimental mode is safer than BACKED but still not as
+  conservative as UNBACKED.
+
 ## Python Code Compilation
 
 In the very verbose logs, we can see:
@@ -122,7 +225,7 @@ When all the shapes are known, `torch.compile` can compare different configs, an
       triton_mm_4 0.0130 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, B_PROLOGUE_CAST_TYPE=None, EVEN_K=True, GROUP_M=8, num_stages=5, num_warps=2
       triton_mm_8 0.0134 ms 97.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=64, B_PROLOGUE_CAST_TYPE=None, EVEN_K=True, GROUP_M=8, num_stages=5, num_warps=4
       triton_mm_12 0.0148 ms 87.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=128, B_PROLOGUE_CAST_TYPE=None, EVEN_K=True, GROUP_M=8, num_stages=4, num_warps=4
-      mm 0.0160 ms 81.6% 
+      mm 0.0160 ms 81.6%
       triton_mm_16 0.0165 ms 78.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=128, B_PROLOGUE_CAST_TYPE=None, EVEN_K=True, GROUP_M=8, num_stages=5, num_warps=8
       triton_mm_3 0.0199 ms 65.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, B_PROLOGUE_CAST_TYPE=None, EVEN_K=True, GROUP_M=8, num_stages=5, num_warps=2
       triton_mm_1 0.0203 ms 64.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, B_PROLOGUE_CAST_TYPE=None, EVEN_K=True, GROUP_M=8, num_stages=2, num_warps=2

tests/compile/test_dynamic_shapes_compilation.py

@@ -0,0 +1,88 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import gc
+
+import pytest
+import torch
+
+from vllm import LLM, SamplingParams
+from vllm.config.compilation import CompilationMode, DynamicShapesType
+from vllm.transformers_utils.tokenizer import get_tokenizer
+from vllm.utils.torch_utils import is_torch_equal_or_newer
+
+
+def get_test_models():
+    """Get list of models to test based on PyTorch version"""
+    # TODO "Qwen/Qwen3-4B-Instruct-2507" fails Fix issue and support it.
+    return ["gpt2", "Qwen/Qwen2-7B-Instruct", "meta-llama/Llama-3.1-8B"]
+
+
+@pytest.mark.parametrize("model_name", get_test_models())
+@pytest.mark.parametrize(
+    "shapes_type",
+    [
+        DynamicShapesType.BACKED,
+        DynamicShapesType.UNBACKED,
+        DynamicShapesType.BACKED_SIZE_OBLIVIOUS,
+    ],
+)
+@pytest.mark.parametrize("use_aot_compile", ["0"])
+@pytest.mark.parametrize("use_bytecode_hook", [True, False])
+@pytest.mark.skipif(
+    not is_torch_equal_or_newer("2.10.0.dev"), reason="requires torch 2.10"
+)
+def test_dynamic_shapes_compilation(
+    monkeypatch, model_name, shapes_type, use_aot_compile, use_bytecode_hook
+):
+    """Test that all dynamic shapes types compile successfully"""
+    print(
+        f"\nTesting model: {model_name} with {shapes_type.name}, "
+        f"AOT compile: {use_aot_compile}, "
+        f"Bytecode hook: {use_bytecode_hook}"
+    )
+    if use_bytecode_hook and shapes_type == DynamicShapesType.UNBACKED:
+        pytest.skip("UNBACKED dynamic shapes require VLLM_USE_BYTECODE_HOOK=0")
+
+    monkeypatch.setenv("VLLM_USE_AOT_COMPILE", use_aot_compile)
+    monkeypatch.setenv("VLLM_USE_BYTECODE_HOOK", "1" if use_bytecode_hook else "0")
+
+    prompt = "Hello, my name is"
+
+    print(f"Testing {shapes_type.name} dynamic shapes...")
+
+    # Initialize the model with specific dynamic shapes configuration
+    model = LLM(
+        model=model_name,
+        compilation_config={
+            "mode": CompilationMode.VLLM_COMPILE,
+            "dynamic_shapes_config": {
+                "type": shapes_type.value,
+            },
+        },
+    )
+
+    output = model.generate(prompt)
+    result = output[0].outputs[0].text
+    # Example of setting the sampling parameters
+    tokenizer = get_tokenizer(model_name)
+    yes_tokens = tokenizer.encode("yes", add_special_tokens=False)
+    no_tokens = tokenizer.encode("no", add_special_tokens=False)
+    allowed_ids = list(set(yes_tokens + no_tokens))
+    sampling_params = SamplingParams(
+        max_tokens=1, temperature=0, allowed_token_ids=allowed_ids
+    )
+
+    output = model.generate(
+        "answer with yes or no is " + result + " rubbish for prompt " + prompt + "?",
+        sampling_params=sampling_params,
+    )
+    result = output[0].outputs[0].text
+    assert result == "yes"
+
+    # Clean up GPU memory
+    del model
+    gc.collect()
+    torch.cuda.empty_cache()
+    torch.cuda.synchronize()
+    print("GPU memory cleared")