Add Flux fp4 support

examples/apps/README.md

@@ -23,6 +23,11 @@ python flux_demo.py
 
 ### Using Different Precision Modes
 
+- FP4 mode:
+```bash
+python flux_demo.py --dtype fp4
+```
+
 - FP8 mode:
 ```bash
 python flux_demo.py --dtype fp8

examples/apps/flux_demo.py

@@ -12,10 +12,6 @@
 from diffusers import FluxPipeline
 from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel
 
-# Register SDPA as a standalone operator. Converter and lowering pass are defined in register_sdpa.py
-sys.path.append(os.path.join(os.path.dirname(__file__), "../dynamo"))
-from register_sdpa import *
-
 DEVICE = "cuda:0"
 
 
@@ -24,8 +20,23 @@ def compile_model(
 ) -> tuple[
     FluxPipeline, FluxTransformer2DModel, torch_tensorrt.MutableTorchTensorRTModule
 ]:
-
-    if args.dtype == "fp8":
+    use_explicit_typing = False
+    if args.use_sdpa:
+        # currently use sdpa is not working correctly with flux model, so we don't use it
+        # Register SDPA as a standalone operator. Converter and lowering pass are defined in register_sdpa.py
+        sys.path.append(os.path.join(os.path.dirname(__file__), "../dynamo"))
+        import register_sdpa
+
+    if args.dtype == "fp4":
+        use_explicit_typing = True
+        enabled_precisions = {torch.float4_e2m1fn_x2}
+        ptq_config = mtq.NVFP4_DEFAULT_CFG
+        if args.fp4_mha:
+            from modelopt.core.torch.quantization.config import NVFP4_FP8_MHA_CONFIG
+
+            ptq_config = NVFP4_FP8_MHA_CONFIG
+
+    elif args.dtype == "fp8":
         enabled_precisions = {torch.float8_e4m3fn, torch.float16}
         ptq_config = mtq.FP8_DEFAULT_CFG
 
@@ -107,26 +118,33 @@ def forward_loop(mod):
         "enabled_precisions": enabled_precisions,
         "truncate_double": True,
         "min_block_size": 1,
-        "use_python_runtime": True,
+        "use_python_runtime": False,
         "immutable_weights": False,
-        "offload_module_to_cpu": True,
+        "offload_module_to_cpu": args.low_vram_mode,
+        "use_explicit_typing": use_explicit_typing,
     }
     if args.low_vram_mode:
         pipe.remove_all_hooks()
         pipe.enable_sequential_cpu_offload()
         remove_hook_from_module(pipe.transformer, recurse=True)
         pipe.transformer.to(DEVICE)
+
     trt_gm = torch_tensorrt.MutableTorchTensorRTModule(backbone, **settings)
     if dynamic_shapes:
         trt_gm.set_expected_dynamic_shape_range((), dynamic_shapes)
     pipe.transformer = trt_gm
-
+    seed = 42
     image = pipe(
-        "Test",
+        [
+            "enchanted winter forest, soft diffuse light on a snow-filled day, serene nature scene, the forest is illuminated by the snow"
+        ],
         output_type="pil",
-        num_inference_steps=2,
+        num_inference_steps=30,
         num_images_per_prompt=batch_size,
+        generator=torch.Generator("cuda").manual_seed(seed),
     ).images
+    print(f"generated {len(image)} images")
+    image[0].save("forest.png")
 
     torch.cuda.empty_cache()
 
@@ -242,12 +260,22 @@ def main(args):
     parser = argparse.ArgumentParser(
         description="Run Flux quantization with different dtypes"
     )
-
+    parser.add_argument(
+        "--use_sdpa",
+        action="store_true",
+        help="Use sdpa",
+        default=False,
+    )
     parser.add_argument(
         "--dtype",
-        choices=["fp8", "int8", "fp16"],
+        choices=["fp4", "fp8", "int8", "fp16"],
         default="fp16",
-        help="Select the data type to use (fp8 or int8 or fp16)",
+        help="Select the data type to use (fp4 or fp8 or int8 or fp16)",
+    )
+    parser.add_argument(
+        "--fp4_mha",
+        action="store_true",
+        help="Use NVFP4_FP8_MHA_CONFIG config instead of NVFP4_DEFAULT_CFG",
     )
     parser.add_argument(
         "--low_vram_mode",

py/torch_tensorrt/dynamo/_compiler.py

@@ -258,10 +258,11 @@ def cross_compile_for_windows(
 
     if use_explicit_typing:
         if len(enabled_precisions) != 1 or not any(
-            x in enabled_precisions for x in {torch.float32, dtype.f32}
+            x in enabled_precisions
+            for x in {torch.float32, dtype.f32, torch.float4_e2m1fn_x2, dtype.f4}
         ):
             raise AssertionError(
-                f"use_explicit_typing was set to True, however found that enabled_precisions was also specified (saw: {enabled_precisions}, expected: {_defaults.ENABLED_PRECISIONS}). enabled_precisions should not be used when use_explicit_typing=True"
+                f"use_explicit_typing was set to True, however found that enabled_precisions was also specified (saw: {enabled_precisions}, expected: dtype.f32, dtype.f4). enabled_precisions should not be used when use_explicit_typing=True"
             )
 
     if use_fp32_acc:
@@ -591,10 +592,11 @@ def compile(
 
     if use_explicit_typing:
         if len(enabled_precisions) != 1 or not any(
-            x in enabled_precisions for x in {torch.float32, dtype.f32}
+            x in enabled_precisions
+            for x in {torch.float32, dtype.f32, torch.float4_e2m1fn_x2, dtype.f4}
         ):
             raise AssertionError(
-                f"use_explicit_typing was set to True, however found that enabled_precisions was also specified (saw: {enabled_precisions}, expected: {_defaults.ENABLED_PRECISIONS}). enabled_precisions should not be used when use_explicit_typing=True"
+                f"use_explicit_typing was set to True, however found that enabled_precisions was also specified (saw: {enabled_precisions}, expected: dtype.f32, dtype.f4). enabled_precisions should not be used when use_explicit_typing=True"
             )
 
     if use_fp32_acc:

py/torch_tensorrt/dynamo/lowering/passes/constant_folding.py

@@ -106,7 +106,11 @@ def __init__(self, *args: Any, **kwargs: Any) -> None:
             import modelopt.torch.quantization as mtq
 
             assert torch.ops.tensorrt.quantize_op.default
+            assert torch.ops.tensorrt.dynamic_block_quantize_op.default
             self.quantization_ops.add(torch.ops.tensorrt.quantize_op.default)
+            self.quantization_ops.add(
+                torch.ops.tensorrt.dynamic_block_quantize_op.default
+            )
         except Exception as e:
             pass
 

py/torch_tensorrt/dynamo/runtime/_MutableTorchTensorRTModule.py

@@ -334,7 +334,7 @@ def export_fn() -> torch.export.ExportedProgram:
         # Check if any quantization precision is enabled
         if self.enabled_precisions and any(
             precision in self.enabled_precisions
-            for precision in (torch.float8_e4m3fn, torch.int8)
+            for precision in (torch.float8_e4m3fn, torch.int8, torch.float4_e2m1fn_x2)
         ):
             try:
                 from modelopt.torch.quantization.utils import export_torch_mode

tools/perf/Flux/flux_perf.py

@@ -3,12 +3,47 @@
 import sys
 from time import time
 
+import torch
+
 sys.path.append(os.path.join(os.path.dirname(__file__), "../../../examples/apps"))
 from flux_demo import compile_model
 
 
+def profile(pipe, prompt, inference_step, batch_size=1):
+    print(f"Running torch profiler with {inference_step=} {batch_size=}")
+    with torch.profiler.profile(
+        activities=[torch.profiler.ProfilerActivity.CUDA],
+        record_shapes=True,
+        profile_memory=True,
+        with_stack=True,
+    ) as prof:
+        with torch.profiler.record_function("model_inference"):
+            pipe(
+                prompt,
+                output_type="pil",
+                num_inference_steps=inference_step,
+                num_images_per_prompt=batch_size,
+            ).images
+    print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=100))
+
+
 def benchmark(pipe, prompt, inference_step, batch_size=1, iterations=1):
+    print(f"Running warmup with {batch_size=} {inference_step=} iterations=10")
+    # warmup
+    for i in range(10):
+        start = time()
+        images = pipe(
+            prompt,
+            output_type="pil",
+            num_inference_steps=inference_step,
+            num_images_per_prompt=batch_size,
+        ).images
+        print(
+            f"Warmup {i} done in {time() - start} seconds, with {batch_size=} {inference_step=}, generated {len(images)} images"
+        )
 
+    # actual benchmark
+    print(f"Running benchmark with {batch_size=} {inference_step=} {iterations=}")
     start = time()
     for i in range(iterations):
         image = pipe(
@@ -18,32 +53,43 @@ def benchmark(pipe, prompt, inference_step, batch_size=1, iterations=1):
             num_images_per_prompt=batch_size,
         ).images
     end = time()
-
     print(f"Batch Size: {batch_size}")
     print("Time Elapse for", iterations, "iterations:", end - start)
     print(
         "Average Latency Per Step:",
         (end - start) / inference_step / iterations / batch_size,
     )
-    return image
+    return
 
 
 def main(args):
+    print(f"Running flux_perfwith args: {args}")
     pipe, backbone, trt_gm = compile_model(args)
-    for batch_size in range(1, args.max_batch_size + 1):
-        benchmark(pipe, ["Test"], 20, batch_size=batch_size, iterations=3)
+
+    benchmark(pipe, ["Test"], 20, batch_size=args.max_batch_size, iterations=3)
+    # profile(pipe, ["enchanted winter forest, soft diffuse light on a snow-filled day, serene nature scene, the forest is illuminated by the snow"], 20, batch_size=args.max_batch_size)
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
         description="Run Flux quantization with different dtypes"
     )
-
+    parser.add_argument(
+        "--use_sdpa",
+        action="store_true",
+        help="Use sdpa",
+        default=False,
+    )
     parser.add_argument(
         "--dtype",
-        choices=["fp8", "int8", "fp16"],
+        choices=["fp4", "fp8", "int8", "fp16"],
         default="fp16",
-        help="Select the data type to use (fp8 or int8 or fp16)",
+        help="Select the data type to use (fp4 or fp8 or int8 or fp16)",
+    )
+    parser.add_argument(
+        "--fp4_mha",
+        action="store_true",
+        help="Use NVFP4_FP8_MHA_CONFIG config instead of NVFP4_DEFAULT_CFG",
     )
     parser.add_argument(
         "--low_vram_mode",