[Model] Support DeepSeek-V4

vllm_mlu/benchmarks/__init__.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-MLU project
+

vllm_mlu/benchmarks/datasets.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-MLU project
+"""
+This module defines a framework for sampling benchmark requests from various
+datasets. Each dataset subclass of BenchmarkDataset must implement sample
+generation. Supported dataset types include:
+  - ShareGPT
+  - Random (synthetic)
+  - Sonnet
+  - BurstGPT
+  - HuggingFace
+  - VisionArena
+"""
+
+from tempfile import NamedTemporaryFile
+
+import numpy as np
+
+from vllm.benchmarks.datasets import RandomMultiModalDataset
+from vllm_mlu.mlu_hijack_utils import MluHijackObject
+
+
+def vllm__benchmarks__datasets__RandomMultiModalDataset__generate_synthetic_video(
+        self, width: int, height: int, num_frames: int
+    ) -> dict:
+        """Generate synthetic video with random values.
+
+        Creates a video with random pixel values, encodes it to MP4 format,
+        and returns the content as bytes.
+        """
+        import cv2
+
+        random_pixels = self._rng.integers(
+            0,
+            256,
+            (num_frames, height, width, 3),
+            dtype=np.uint8,
+        )
+
+        # Create a temporary video file in memory
+        fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+        fps = 30  # frames per second
+
+        with NamedTemporaryFile(suffix=".mp4", delete=False) as temp_file:
+            temp_path = temp_file.name
+
+            # Create video writer
+            video_writer = cv2.VideoWriter(
+                temp_path, fourcc=fourcc, fps=fps, frameSize=(width, height)
+            )
+
+            if not video_writer.isOpened():
+                raise RuntimeError("Failed to create video writer")
+
+            for frame in random_pixels:
+                video_writer.write(frame)
+
+            video_writer.release()
+            temp_file.close()
+
+            # Read the video file content
+            with open(temp_path, "rb") as f:
+                video_content = f.read()
+
+            return {"bytes": video_content}
+        
+
+MluHijackObject.apply_hijack(
+    RandomMultiModalDataset,
+    RandomMultiModalDataset.generate_synthetic_video,
+    vllm__benchmarks__datasets__RandomMultiModalDataset__generate_synthetic_video,
+)