skip detokenize

.buildkite/lm-eval-harness/configs/Meta-Llama-4-Maverick-17B-128E-Instruct-FP8-MM.yaml

@@ -1,12 +1,11 @@
 # For hf script, without -t option (tensor parallel size).
-# bash .buildkite/lm-eval-harness/run-lm-eval-chartqa-vllm-vlm-baseline.sh -m meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 -l 100 -t 8
+# bash .buildkite/lm-eval-harness/run-lm-eval-chartqa-vllm-vlm-baseline.sh -m meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 -b 32 -l 100 -t 8
 model_name: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
 backend: "vllm-vlm"
 tasks:
 - name: "chartqa"
   metrics:
   - name: "relaxed_accuracy,none"
-    # TODO(zhewenl): model card is 0.90, but the actual score is 0.80.
-    value: 0.80
+    value: 0.90
 limit: 100
 num_fewshot: 0

.buildkite/lm-eval-harness/configs/Meta-Llama-4-Maverick-17B-128E-Instruct-FP8.yaml

@@ -1,6 +1,7 @@
 # For hf script, without -t option (tensor parallel size).
-# bash .buildkite/lm-eval-harness/run-lm-eval-mmlupro-vllm-baseline.sh -m meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 -l 250 -t 8 -f 5
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 -b 32 -l 250 -t 8 -f 5
 model_name: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
+backend: "vllm-vlm"
 tasks:
 - name: "mmlu_pro"
   metrics:

.buildkite/nightly-benchmarks/scripts/compare-json-results.py

@@ -7,7 +7,6 @@ from importlib import util
 
 import pandas as pd
 
-pd.options.display.float_format = "{:.2f}".format
 plotly_found = util.find_spec("plotly.express") is not None
 
 
@@ -110,10 +109,7 @@ def compare_data_columns(
         if len(compare_frames) >= 2:
             base = compare_frames[0]
             current = compare_frames[-1]
-            if "P99" in data_column or "Median" in data_column:
-                ratio = base / current  # for latency
-            else:
-                ratio = current / base
+            ratio = current / base
             ratio = ratio.mask(base == 0)  # avoid inf when baseline is 0
             ratio.name = f"Ratio 1 vs {len(compare_frames)}"
             frames.append(ratio)
@@ -203,71 +199,6 @@ def split_json_by_tp_pp(
     return saved_paths
 
 
-def _add_limit_line(fig, y_value, label):
-    # Visible dashed line + annotation
-    fig.add_hline(
-        y=y_value,
-        line_dash="dash",
-        line_color="red" if "ttft" in label.lower() else "blue",
-        annotation_text=f"{label}: {y_value} ms",
-        annotation_position="top left",
-    )
-    # Optional: add a legend item (as a transparent helper trace)
-    if plot and plotly_found:
-        import plotly.graph_objects as go
-
-        fig.add_trace(
-            go.Scatter(
-                x=[None],
-                y=[None],
-                mode="lines",
-                line=dict(
-                    dash="dash", color="red" if "ttft" in label.lower() else "blue"
-                ),
-                name=f"{label}",
-            )
-        )
-
-
-def _find_concurrency_col(df: pd.DataFrame) -> str:
-    for c in [
-        "# of max concurrency.",
-        "# of max concurrency",
-        "Max Concurrency",
-        "max_concurrency",
-        "Concurrency",
-    ]:
-        if c in df.columns:
-            return c
-    # Fallback: guess an integer-like column (harmless if unused)
-    for c in df.columns:
-        if df[c].dtype.kind in "iu" and df[c].nunique() > 1 and df[c].min() >= 1:
-            return c
-    return "# of max concurrency."
-
-
-def _highlight_threshold(
-    df: pd.DataFrame, threshold: float
-) -> "pd.io.formats.style.Styler":
-    """Highlight numeric per-configuration columns with value <= threshold."""
-    conc_col = _find_concurrency_col(df)
-    key_cols = [
-        c
-        for c in ["Model", "Dataset Name", "Input Len", "Output Len", conc_col]
-        if c in df.columns
-    ]
-    conf_cols = [
-        c for c in df.columns if c not in key_cols and not str(c).startswith("Ratio")
-    ]
-    conf_cols = [c for c in conf_cols if pd.api.types.is_numeric_dtype(df[c])]
-    return df.style.map(
-        lambda v: "background-color:#e6ffe6;font-weight:bold;"
-        if pd.notna(v) and v <= threshold
-        else "",
-        subset=conf_cols,
-    )
-
-
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument(
@@ -289,26 +220,6 @@ if __name__ == "__main__":
         default="# of max concurrency.",
         help="column name to use as X Axis in comparison graph",
     )
-    parser.add_argument(
-        "-l",
-        "--latency",
-        type=str,
-        default="p99",
-        help="take median|p99 for latency like TTFT/TPOT",
-    )
-    parser.add_argument(
-        "--ttft-max-ms",
-        type=float,
-        default=3000.0,
-        help="Reference limit for TTFT plots (ms)",
-    )
-    parser.add_argument(
-        "--tpot-max-ms",
-        type=float,
-        default=100.0,
-        help="Reference limit for TPOT plots (ms)",
-    )
-
     args = parser.parse_args()
 
     drop_column = "P99"
@@ -323,22 +234,12 @@ if __name__ == "__main__":
         "# of max concurrency.",
         "qps",
     ]
-
-    if "median" in args.latency:
-        data_cols_to_compare = ["Output Tput (tok/s)", "Median TTFT (ms)", "Median"]
-        html_msgs_for_data_cols = [
-            "Compare Output Tokens /n",
-            "Median TTFT /n",
-            "Median TPOT /n",
-        ]
-        drop_column = "P99"
-    elif "p99" in args.latency:
-        data_cols_to_compare = ["Output Tput (tok/s)", "P99 TTFT (ms)", "P99"]
-        html_msgs_for_data_cols = [
-            "Compare Output Tokens /n",
-            "P99 TTFT /n",
-            "P99 TPOT /n",
-        ]
+    data_cols_to_compare = ["Output Tput (tok/s)", "Median TTFT (ms)", "Median"]
+    html_msgs_for_data_cols = [
+        "Compare Output Tokens /n",
+        "Median TTFT /n",
+        "Median TPOT /n",
+    ]
 
     if len(args.file) == 1:
         files = split_json_by_tp_pp(args.file[0], output_root="splits")
@@ -374,83 +275,33 @@ if __name__ == "__main__":
                     f"Expected subset: {filtered_info_cols}, "
                     f"but DataFrame has: {list(output_df.columns)}"
                 )
-            # output_df_sorted = output_df.sort_values(by=existing_group_cols)
-            output_df_sorted = output_df.sort_values(by=args.xaxis)
+            output_df_sorted = output_df.sort_values(by=existing_group_cols)
             output_groups = output_df_sorted.groupby(existing_group_cols, dropna=False)
             for name, group in output_groups:
-                group_name = (
-                    ",".join(map(str, name)).replace(",", "_").replace("/", "-")
-                )
-                group_html_name = "perf_comparison_" + group_name + ".html"
-
-                metric_name = str(data_cols_to_compare[i]).lower()
-                if "tok/s" in metric_name:
-                    html = group.to_html()
-                elif "ttft" in metric_name:
-                    styler = _highlight_threshold(group, args.ttft_max_ms).format(
-                        {c: "{:.2f}" for c in group.select_dtypes("number").columns},
-                        na_rep="—",
-                    )
-                    html = styler.to_html(
-                        table_attributes='border="1" class="dataframe"'
-                    )
-                elif (
-                    "tpot" in metric_name
-                    or "median" in metric_name
-                    or "p99" in metric_name
-                ):
-                    styler = _highlight_threshold(group, args.tpot_max_ms).format(
-                        {c: "{:.2f}" for c in group.select_dtypes("number").columns},
-                        na_rep="—",
-                    )
-                    html = styler.to_html(
-                        table_attributes='border="1" class="dataframe"'
-                    )
-
+                html = group.to_html()
                 text_file.write(html_msgs_for_data_cols[i])
                 text_file.write(html)
-                with open(group_html_name, "a+") as sub_text_file:
-                    sub_text_file.write(html_msgs_for_data_cols[i])
-                    sub_text_file.write(html)
 
-                    if plot and plotly_found:
-                        import plotly.express as px
+                if plot and plotly_found:
+                    import plotly.express as px
 
-                        df = group[raw_data_cols]
-                        df_sorted = df.sort_values(by=info_cols[y_axis_index])
-                        # Melt DataFrame for plotting
-                        df_melted = df_sorted.melt(
-                            id_vars=info_cols[y_axis_index],
-                            var_name="Configuration",
-                            value_name=data_cols_to_compare[i],
-                        )
-                        title = (
-                            data_cols_to_compare[i] + " vs " + info_cols[y_axis_index]
-                        )
-                        # Create Plotly line chart
-                        fig = px.line(
-                            df_melted,
-                            x=info_cols[y_axis_index],
-                            y=data_cols_to_compare[i],
-                            color="Configuration",
-                            title=title,
-                            markers=True,
-                        )
-
-                        # ---- Add threshold lines based on metric name ----
-                        if "ttft" in metric_name:
-                            _add_limit_line(fig, args.ttft_max_ms, "TTFT limit")
-                        elif (
-                            "tpot" in metric_name
-                            or "median" in metric_name
-                            or "p99" in metric_name
-                        ):
-                            _add_limit_line(fig, args.tpot_max_ms, "TPOT limit")
-
-                        # Export to HTML
-                        text_file.write(
-                            fig.to_html(full_html=True, include_plotlyjs="cdn")
-                        )
-                        sub_text_file.write(
-                            fig.to_html(full_html=True, include_plotlyjs="cdn")
-                        )
+                    df = group[raw_data_cols]
+                    df_sorted = df.sort_values(by=info_cols[y_axis_index])
+                    # Melt DataFrame for plotting
+                    df_melted = df_sorted.melt(
+                        id_vars=info_cols[y_axis_index],
+                        var_name="Configuration",
+                        value_name=data_cols_to_compare[i],
+                    )
+                    title = data_cols_to_compare[i] + " vs " + info_cols[y_axis_index]
+                    # Create Plotly line chart
+                    fig = px.line(
+                        df_melted,
+                        x=info_cols[y_axis_index],
+                        y=data_cols_to_compare[i],
+                        color="Configuration",
+                        title=title,
+                        markers=True,
+                    )
+                    # Export to HTML
+                    text_file.write(fig.to_html(full_html=True, include_plotlyjs="cdn"))

.buildkite/nightly-benchmarks/scripts/convert-results-json-to-markdown.py

@@ -63,11 +63,9 @@ serving_column_mapping = {
     "mean_ttft_ms": "Mean TTFT (ms)",
     "median_ttft_ms": "Median TTFT (ms)",
     "p99_ttft_ms": "P99 TTFT (ms)",
-    "std_ttft_ms": "STD TTFT (ms)",
     "mean_tpot_ms": "Mean TPOT (ms)",
     "median_tpot_ms": "Median",
     "p99_tpot_ms": "P99",
-    "std_tpot_ms": "STD TPOT (ms)",
     "mean_itl_ms": "Mean ITL (ms)",
     "median_itl_ms": "Median ITL (ms)",
     "p99_itl_ms": "P99 ITL (ms)",
@@ -370,7 +368,7 @@ if __name__ == "__main__":
         # The GPUs sometimes come in format of "GPUTYPE\nGPUTYPE\n...",
         # we want to turn it into "8xGPUTYPE"
         df["GPU"] = df["GPU"].apply(
-            lambda x: "{}x{}".format(len(x.split("\n")), x.split("\n")[0])
+            lambda x: f"{len(x.splitlines())}x{x.splitlines()[0]}"
         )
 
     # get markdown tables

.buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh

@@ -471,11 +471,6 @@ main() {
   mkdir -p $RESULTS_FOLDER
   QUICK_BENCHMARK_ROOT=../.buildkite/nightly-benchmarks/
 
-  # dump vllm info via vllm collect-env
-  env_output=$(vllm collect-env)
-
-  echo "$env_output" >"$RESULTS_FOLDER/vllm_env.txt"
-
   # benchmarking
   run_serving_tests $QUICK_BENCHMARK_ROOT/tests/"${SERVING_JSON:-serving-tests$ARCH.json}"
   run_latency_tests $QUICK_BENCHMARK_ROOT/tests/"${LATENCY_JSON:-latency-tests$ARCH.json}"

.buildkite/nightly-benchmarks/tests/latency-tests-cpu.json

@@ -1,24 +1,28 @@
 [
     {
-        "test_name": "latency_llama8B_tp2",
+        "test_name": "latency_llama8B_tp1",
         "environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "parameters": {
             "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 2,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
+            "tensor_parallel_size": 1,
+            "load_format": "dummy",
+            "num_iters_warmup": 5,
+            "num_iters": 15
+        }
+    },
+    {
+        "test_name": "latency_llama8B_tp4",
+        "environment_variables": {
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 4,
+            "load_format": "dummy",
             "num_iters_warmup": 5,
             "num_iters": 15
         }

.buildkite/nightly-benchmarks/tests/serving-tests-cpu-snc3.json

@@ -95,38 +95,6 @@
             "num_prompts": 200
         }
     },
-    {
-        "test_name": "serving_llama8B_bf16_tp4_sharegpt",
-        "qps_list": ["inf"],
-        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 4,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 200
-        }
-    },
     {
         "test_name": "serving_llama8B_bf16_tp2pp3_sharegpt",
         "qps_list": ["inf"],
@@ -265,41 +233,6 @@
             "num_prompts": 1000
         }
     },
-    {
-        "test_name": "serving_llama8B_bf16_tp4_random_128_128",
-        "qps_list": ["inf"],
-        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 4,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-            "num_prompts": 1000
-        }
-    },
     {
         "test_name": "serving_llama8B_bf16_tp2pp3_random_128_128",
         "qps_list": ["inf"],
@@ -432,38 +365,6 @@
             "num_prompts": 200
         }
     },
-    {
-        "test_name": "serving_llama8B_int8_tp4_sharegpt",
-        "qps_list": ["inf"],
-        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
-            "tensor_parallel_size": 4,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 200
-        }
-    },
     {
         "test_name": "serving_llama8B_int8_tp2pp3_sharegpt",
         "qps_list": ["inf"],
@@ -602,41 +503,6 @@
             "num_prompts": 1000
         }
     },
-    {
-        "test_name": "serving_llama8B_int8_tp4_random_128_128",
-        "qps_list": ["inf"],
-        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
-            "tensor_parallel_size": 4,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-            "num_prompts": 1000
-        }
-    },
     {
         "test_name": "serving_llama8B_int8_tp2pp3_random_128_128",
         "qps_list": ["inf"],
@@ -772,39 +638,6 @@
             "num_prompts": 200
         }
     },
-    {
-        "test_name": "serving_llama8B_int4_tp4_sharegpt",
-        "qps_list": ["inf"],
-        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
-	    "quantization": "awq",
-            "tensor_parallel_size": 4,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 200
-        }
-    },
     {
         "test_name": "serving_llama8B_int4_tp2pp3_sharegpt",
         "qps_list": ["inf"],
@@ -947,42 +780,6 @@
             "num_prompts": 1000
         }
     },
-    {
-        "test_name": "serving_llama8B_int4_tp4_random_128_128",
-        "qps_list": ["inf"],
-        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
-	    "quantization": "awq",
-            "tensor_parallel_size": 4,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-            "num_prompts": 1000
-        }
-    },
     {
         "test_name": "serving_llama8B_int4_tp2pp3_random_128_128",
         "qps_list": ["inf"],

.buildkite/nightly-benchmarks/tests/serving-tests-cpu.json

@@ -2,7 +2,7 @@
     {
         "test_name": "serving_llama8B_tp1_sharegpt",
         "qps_list": [1, 4, 16, "inf"],
-        "max_concurrency_list": [32],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -28,13 +28,13 @@
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 32
+            "num_prompts": 200
         }
     },
     {
         "test_name": "serving_llama8B_tp2_sharegpt",
         "qps_list": [1, 4, 16, "inf"],
-        "max_concurrency_list": [32],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -60,13 +60,13 @@
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 32
+            "num_prompts": 200
         }
     },
     {
-        "test_name": "serving_llama8B_tp1_random_128_128",
+        "test_name": "serving_llama8B_tp4_sharegpt",
         "qps_list": [1, 4, 16, "inf"],
-        "max_concurrency_list": [32],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -76,7 +76,39 @@
         },
         "server_parameters": {
             "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
+            "tensor_parallel_size": 4,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_tp4_random_1024_128",
+        "qps_list": [1, 4, 16, "inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 4,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
 	    "block_size": 128,
@@ -92,16 +124,16 @@
             "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "random",
-	    "random-input-len": 128,
+	    "random-input-len": 1024,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-            "num_prompts": 32
+            "num_prompts": 100
         }
     },
     {
-        "test_name": "serving_llama8B_tp2_random_128_128",
+        "test_name": "serving_llama8B_pp6_random_1024_128",
         "qps_list": [1, 4, 16, "inf"],
-        "max_concurrency_list": [32],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -111,7 +143,7 @@
         },
         "server_parameters": {
             "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 2,
+            "pipeline_parallel_size": 6,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
 	    "block_size": 128,
@@ -127,150 +159,10 @@
             "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "random",
-	    "random-input-len": 128,
+	    "random-input-len": 1024,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-            "num_prompts": 32
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp1_random_128_2048",
-        "qps_list": [1, 4, 16, "inf"],
-        "max_concurrency_list": [32],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 2048,
-	    "ignore-eos": "",
-            "num_prompts": 32
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp2_random_128_2048",
-        "qps_list": [1, 4, 16, "inf"],
-        "max_concurrency_list": [32],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 2,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 2048,
-	    "ignore-eos": "",
-            "num_prompts": 32
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp1_random_2048_128",
-        "qps_list": [1, 4, 16, "inf"],
-        "max_concurrency_list": [32],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 2048,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-            "num_prompts": 32
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp2_random_2048_128",
-        "qps_list": [1, 4, 16, "inf"],
-        "max_concurrency_list": [32],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 2,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 2048,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-            "num_prompts": 32
+            "num_prompts": 100
         }
     }
 ]

.buildkite/nightly-benchmarks/tests/throughput-tests-cpu.json

@@ -1,24 +1,29 @@
 [
     {
-        "test_name": "throughput_llama8B_tp2",
+        "test_name": "throughput_llama8B_tp1",
         "environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "parameters": {
             "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 2,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
+            "tensor_parallel_size": 1,
+            "load_format": "dummy",
+            "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200,
+            "backend": "vllm"
+        }
+    },
+    {
+        "test_name": "throughput_llama8B_tp4",
+        "environment_variables": {
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 4,
+            "load_format": "dummy",
             "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
             "num_prompts": 200,
             "backend": "vllm"

.buildkite/release-pipeline.yaml

@@ -1,5 +1,5 @@
 steps:
-  # aarch64 + CUDA builds
+  # aarch64 + CUDA builds. PyTorch 2.8 aarch64 + CUDA wheel is only available on CUDA 12.9
   - label: "Build arm64 wheel - CUDA 12.9"
     depends_on: ~
     id: build-wheel-arm64-cuda-12-9
@@ -15,21 +15,6 @@ steps:
     env:
       DOCKER_BUILDKIT: "1"
 
-  # aarch64 build
-  - label: "Build arm64 CPU wheel"
-    depends_on: ~
-    id: build-wheel-arm64-cpu
-    agents:
-      queue: arm64_cpu_queue_postmerge
-    commands:
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_BUILD_ACL=ON --tag vllm-ci:build-image --target vllm-build --progress plain -f docker/Dockerfile.cpu ."
-      - "mkdir artifacts"
-      - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/scripts/upload-wheels.sh"
-    env:
-      DOCKER_BUILDKIT: "1"
-
-  # x86 + CUDA builds
   - label: "Build wheel - CUDA 12.8"
     depends_on: ~
     id: build-wheel-cuda-12-8
@@ -43,6 +28,20 @@ steps:
     env:
       DOCKER_BUILDKIT: "1"
 
+  - label: "Build wheel - CUDA 12.6"
+    depends_on: ~
+    id: build-wheel-cuda-12-6
+    agents:
+      queue: cpu_queue_postmerge
+    commands:
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.6.3 --build-arg torch_cuda_arch_list='7.0 7.5 8.0 8.9 9.0+PTX' --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+      - "mkdir artifacts"
+      - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
+      - "bash .buildkite/scripts/upload-wheels.sh"
+    env:
+      DOCKER_BUILDKIT: "1"
+
+  # x86 + CUDA builds
   - label: "Build wheel - CUDA 12.9"
     depends_on: ~
     id: build-wheel-cuda-12-9
@@ -56,20 +55,6 @@ steps:
     env:
       DOCKER_BUILDKIT: "1"
 
-  - label: "Build wheel - CUDA 13.0"
-    depends_on: ~
-    id: build-wheel-cuda-13-0
-    agents:
-      queue: cpu_queue_postmerge
-    commands:
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
-      - "mkdir artifacts"
-      - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/scripts/upload-wheels.sh"
-    env:
-      DOCKER_BUILDKIT: "1"
-
-  # Build release images (12.9)
   - label: "Build release image (x86)"
     depends_on: ~
     id: build-release-image-x86
@@ -77,12 +62,13 @@ steps:
       queue: cpu_queue_postmerge
     commands:
       - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.8.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
       - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
       # re-tag to default image tag and push, just in case arm64 build fails
       - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
       - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
 
+  # PyTorch 2.8 aarch64 + CUDA wheel is only available on CUDA 12.9
   - label: "Build release image (arm64)"
     depends_on: ~
     id: build-release-image-arm64
@@ -156,22 +142,6 @@ steps:
     env:
       DOCKER_BUILDKIT: "1"
 
-  - block: "Build arm64 CPU release image"
-    key: block-arm64-cpu-release-image-build
-    depends_on: ~
-
-  - label: "Build and publish arm64 CPU release image"
-    depends_on: block-arm64-cpu-release-image-build
-    agents:
-      queue: arm64_cpu_queue_postmerge
-    commands:
-      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
-      - "docker push public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:latest"
-      - "docker push public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
-    env:
-      DOCKER_BUILDKIT: "1"
-
   - label: "Build and publish nightly multi-arch image to DockerHub"
     depends_on:
       - create-multi-arch-manifest

.buildkite/scripts/hardware_ci/run-cpu-test.sh

@@ -70,7 +70,7 @@ function cpu_tests() {
   docker exec cpu-test-"$NUMA_NODE" bash -c "
     set -e
     pytest -x -s -v \
-    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs"
+    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs[False-10-32-neuralmagic/Llama-3.2-1B-quantized.w8a8]"
 
   # Note: disable it until supports V1
   # Run AWQ test

.buildkite/scripts/upload-wheels.sh

@@ -58,25 +58,33 @@ python3 .buildkite/generate_index.py --wheel "$normal_wheel"
 aws s3 cp "$wheel" "s3://vllm-wheels/$BUILDKITE_COMMIT/"
 aws s3 cp "$normal_wheel" "s3://vllm-wheels/$BUILDKITE_COMMIT/"
 
-if [[ $normal_wheel == *"cu129"* ]]; then
+if [[ $normal_wheel == *"cu126"* ]]; then
+    # if $normal_wheel matches cu126, do not upload the index.html
+    echo "Skipping index files for cu126 wheels"
+elif [[ $normal_wheel == *"cu128"* ]]; then
+    # if $normal_wheel matches cu128, do not upload the index.html
+    echo "Skipping index files for cu128 wheels"
+else
     # only upload index.html for cu129 wheels (default wheels) as it
     # is available on both x86 and arm64
     aws s3 cp index.html "s3://vllm-wheels/$BUILDKITE_COMMIT/vllm/index.html"
     aws s3 cp "s3://vllm-wheels/nightly/index.html" "s3://vllm-wheels/$BUILDKITE_COMMIT/index.html"
-else
-    echo "Skipping index files for non-cu129 wheels"
 fi
 
 # generate index for nightly
 aws s3 cp "$wheel" "s3://vllm-wheels/nightly/"
 aws s3 cp "$normal_wheel" "s3://vllm-wheels/nightly/"
 
-if [[ $normal_wheel == *"cu129"* ]]; then
+if [[ $normal_wheel == *"cu126"* ]]; then
+    # if $normal_wheel matches cu126, do not upload the index.html
+    echo "Skipping index files for cu126 wheels"
+elif [[ $normal_wheel == *"cu128"* ]]; then
+    # if $normal_wheel matches cu128, do not upload the index.html
+    echo "Skipping index files for cu128 wheels"
+else
     # only upload index.html for cu129 wheels (default wheels) as it
     # is available on both x86 and arm64
     aws s3 cp index.html "s3://vllm-wheels/nightly/vllm/index.html"
-else
-    echo "Skipping index files for non-cu129 wheels"
 fi
 
 aws s3 cp "$wheel" "s3://vllm-wheels/$version/"

.buildkite/test-amd.yaml

@@ -50,7 +50,7 @@ steps:
 
 - label: Async Engine, Inputs, Utils, Worker Test # 36min
   timeout_in_minutes: 50
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
   # grade: Blocking
   source_file_dependencies:
@@ -395,9 +395,7 @@ steps:
     - python3 offline_inference/basic/embed.py
     - python3 offline_inference/basic/score.py
     - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
-    # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
-    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
-    #- python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
 
 - label: Platform Tests (CUDA) # 4min
   timeout_in_minutes: 15
@@ -438,11 +436,7 @@ steps:
       --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT \
       --ignore=lora/test_chatglm3_tp.py \
       --ignore=lora/test_llama_tp.py \
-      --ignore=lora/test_llm_with_multi_loras.py \
-      --ignore=lora/test_olmoe_tp.py \
-      --ignore=lora/test_deepseekv2_tp.py \
-      --ignore=lora/test_gptoss.py \
-      --ignore=lora/test_qwen3moe_tp.py
+      --ignore=lora/test_llm_with_multi_loras.py
   parallelism: 4
 
 - label: PyTorch Compilation Unit Tests # 15min
@@ -460,8 +454,8 @@ steps:
     - pytest -v -s compile/test_fusion_attn.py
     - pytest -v -s compile/test_functionalization.py
     - pytest -v -s compile/test_silu_mul_quant_fusion.py
-  #  - pytest -v -s compile/test_sequence_parallelism.py
-  #  - pytest -v -s compile/test_async_tp.py
+    - pytest -v -s compile/test_sequence_parallelism.py
+    - pytest -v -s compile/test_async_tp.py
     - pytest -v -s compile/test_fusion_all_reduce.py
     - pytest -v -s compile/test_decorator.py
     - pytest -v -s compile/test_noop_elimination.py
@@ -480,8 +474,8 @@ steps:
   - pytest -v -s compile/test_basic_correctness.py
   - pytest -v -s compile/piecewise/
 
-- label: PyTorch Fullgraph Test # 22min
-  timeout_in_minutes: 35
+- label: PyTorch Fullgraph Test # 20min
+  timeout_in_minutes: 30
   mirror_hardwares: [amdexperimental, amdproduction]
   agent_pool: mi325_1
   # grade: Blocking
@@ -491,7 +485,6 @@ steps:
   - tests/compile
   commands:
   - pytest -v -s compile/test_full_graph.py
-  - pytest -v -s compile/test_fusions_e2e.py
 
 - label: Kernels Core Operation Test # 48min
   timeout_in_minutes: 75
@@ -501,7 +494,6 @@ steps:
   source_file_dependencies:
   - csrc/
   - tests/kernels/core
-  - tests/kernels/test_top_k_per_row.py
   commands:
     - pytest -v -s kernels/core kernels/test_top_k_per_row.py
 
@@ -561,7 +553,7 @@ steps:
 
 - label: Model Executor Test # 23min
   timeout_in_minutes: 35
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
   # grade: Blocking
   source_file_dependencies:
@@ -614,7 +606,7 @@ steps:
   # we can only upgrade after this is resolved
   # TODO(jerryzh168): resolve the above comment
   - uv pip install --system torchao==0.13.0
-  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py
+  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/
 
 - label: LM Eval Small Models # 53min
   timeout_in_minutes: 75
@@ -789,10 +781,8 @@ steps:
   - vllm/
   - tests/models/language/generation
   commands:
-    # Install fast path packages for testing against transformers
-    # Note: also needed to run plamo2 model in vLLM
-    - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.2.5'
-    - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
+    # Install causal-conv1d for plamo2 models here, as it is not compatible with pip-compile.
+    - pip install 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.0.post8'
     - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
 
 - label: Language Models Test (PPL)
@@ -858,18 +848,6 @@ steps:
     - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/processing
     - cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model  # Otherwise, mp_method="spawn" doesn't work
 
-- label: Multi-Modal Accuracy Eval (Small Models) # 50min
-  mirror_hardwares: [amdexperimental]
-  agent_pool: mi325_1
-  timeout_in_minutes: 70
-  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
-  source_file_dependencies:
-  - vllm/multimodal/
-  - vllm/inputs/
-  - vllm/v1/core/
-  commands:
-  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt --tp-size=1
-
 - label: Multi-Modal Models Test (Extended) 1
   mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
@@ -945,8 +923,8 @@ steps:
     # Whisper needs spawn method to avoid deadlock
     - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
 
-- label: Blackwell Test # 21 min
-  timeout_in_minutes: 30
+- label: Blackwell Test # 38 min
+  timeout_in_minutes: 60
   working_dir: "/vllm-workspace/"
   gpu: b200
   # optional: true
@@ -959,6 +937,8 @@ steps:
   - vllm/model_executor/layers/fused_moe/flashinfer_cutlass_prepare_finalize.py
   - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
   - vllm/v1/attention/backends/flashinfer.py
+  - vllm/compilation/fusion.py
+  - vllm/compilation/fusion_attn.py
   commands:
     - nvidia-smi
     - python3 examples/offline_inference/basic/chat.py
@@ -975,32 +955,13 @@ steps:
     - pytest -v -s tests/kernels/quantization/test_nvfp4_scaled_mm.py
     - pytest -v -s tests/kernels/quantization/test_flashinfer_scaled_mm.py
     - pytest -v -s tests/kernels/quantization/test_flashinfer_nvfp4_scaled_mm.py
-    - pytest -v -s tests/kernels/quantization/test_nvfp4_qutlass.py
-    - pytest -v -s tests/kernels/quantization/test_mxfp4_qutlass.py
     - pytest -v -s tests/kernels/moe/test_nvfp4_moe.py
     - pytest -v -s tests/kernels/moe/test_ocp_mx_moe.py
-    - pytest -v -s tests/kernels/moe/test_flashinfer.py
-
-- label: Blackwell Fusion Tests # 30 min
-  timeout_in_minutes: 40
-  working_dir: "/vllm-workspace/"
-  gpu: b200
-  source_file_dependencies:
-  - csrc/quantization/fp4/
-  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
-  - vllm/v1/attention/backends/flashinfer.py
-  - vllm/compilation/
-  # can affect pattern matching
-  - vllm/model_executor/layers/layernorm.py
-  - vllm/model_executor/layers/activation.py
-  - vllm/model_executor/layers/quantization/input_quant_fp8.py
-  commands:
-    - nvidia-smi
-    - pytest -v -s tests/compile/test_fusion_attn.py
-    - pytest -v -s tests/compile/test_silu_mul_quant_fusion.py
-    # this runner has 2 GPUs available even though num_gpus=2 is not set
+    # Fusion
     - pytest -v -s tests/compile/test_fusion_all_reduce.py
-    - pytest -v -s tests/compile/test_fusions_e2e.py
+    - pytest -v -s tests/compile/test_fusion_attn.py::test_attention_quant_pattern
+    - pytest -v -s tests/kernels/moe/test_flashinfer.py
+    - pytest -v -s tests/compile/test_silu_mul_quant_fusion.py
 
 - label: Blackwell GPT-OSS Eval
   timeout_in_minutes: 60
@@ -1120,7 +1081,6 @@ steps:
   - pytest -v -s ./compile/test_basic_correctness.py
   - pytest -v -s ./compile/test_wrapper.py
   - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
-  - VLLM_TEST_SAME_HOST=1 VLLM_TEST_WITH_DEFAULT_DEVICE_SET=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
   - pytest -v -s distributed/test_sequence_parallel.py
   - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
   - pytest -v -s v1/worker/test_worker_memory_snapshot.py
@@ -1168,11 +1128,6 @@ steps:
   - pytest -v -s plugins_tests/test_io_processor_plugins.py
   - pip uninstall prithvi_io_processor_plugin -y
   # end io_processor plugins test
-  # begin stat_logger plugins test
-  - pip install -e ./plugins/vllm_add_dummy_stat_logger
-  - pytest -v -s plugins_tests/test_stats_logger_plugins.py
-  - pip uninstall dummy_stat_logger -y
-  # end stat_logger plugins test
   # other tests continue here:
   - pytest -v -s plugins_tests/test_scheduler_plugins.py
   - pip install -e ./plugins/vllm_add_dummy_model
@@ -1216,7 +1171,7 @@ steps:
     - pytest -v -s -x lora/test_chatglm3_tp.py
     - pytest -v -s -x lora/test_llama_tp.py
     - pytest -v -s -x lora/test_llm_with_multi_loras.py
-    - pytest -v -s -x lora/test_olmoe_tp.py
+
 
 - label: Weight Loading Multiple GPU Test  # 33min
   timeout_in_minutes: 45
@@ -1246,18 +1201,6 @@ steps:
   commands:
     - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large.txt
 
-- label: NixlConnector PD accuracy tests (Distributed) # 30min
-  mirror_hardwares: [amdexperimental]
-  agent_pool: mi325_4
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - bash v1/kv_connector/nixl_integration/tp_config_sweep_accuracy_test.sh
 
 ##### multi gpus test #####
 ##### A100 test #####
@@ -1289,16 +1232,12 @@ steps:
   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
 
 ##### H200 test #####
-- label: Distributed Tests (H200) # optional
+- label: Distrubted Tests (H200) # optional
   gpu: h200
   optional: true
   working_dir: "/vllm-workspace/"
   num_gpus: 2
   commands:
-    - pytest -v -s tests/compile/test_async_tp.py
-    - pytest -v -s tests/compile/test_sequence_parallelism.py
-    - pytest -v -s tests/compile/test_fusion_all_reduce.py
-    - pytest -v -s tests/compile/test_fusions_e2e.py::test_tp2_attn_quant_allreduce_rmsnorm
     - pytest -v -s tests/distributed/test_context_parallel.py
     - CUDA_VISIBLE_DEVICES=1,2 VLLM_ALL2ALL_BACKEND=deepep_high_throughput VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model Qwen/Qwen1.5-MoE-A2.7B --tp-size=1  --dp-size=2 --max-model-len 2048
 

.buildkite/test-pipeline.yaml

@@ -172,8 +172,6 @@ steps:
   - tests/v1/engine/test_engine_core_client.py
   - tests/distributed/test_symm_mem_allreduce.py
   commands:
-  # https://github.com/NVIDIA/nccl/issues/1838
-  - export NCCL_CUMEM_HOST_ENABLE=0
   # test with torchrun tp=2 and external_dp=2
   - torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
   # test with torchrun tp=2 and pp=2
@@ -313,15 +311,6 @@ steps:
     - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
     - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
 
-- label: V1 Test attention (H100) # 10min
-  timeout_in_minutes: 30
-  gpu: h100
-  source_file_dependencies:
-    - vllm/v1/attention
-    - tests/v1/attention
-  commands:
-    - pytest -v -s v1/attention
-
 - label: V1 Test others (CPU) # 5 mins
   source_file_dependencies:
     - vllm/
@@ -360,8 +349,7 @@ steps:
     - python3 offline_inference/basic/embed.py
     - python3 offline_inference/basic/score.py
     - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
-    # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
-    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
+    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
 
 - label: Platform Tests (CUDA) # 4min
   timeout_in_minutes: 15
@@ -396,12 +384,7 @@ steps:
       --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT \
       --ignore=lora/test_chatglm3_tp.py \
       --ignore=lora/test_llama_tp.py \
-      --ignore=lora/test_llm_with_multi_loras.py \
-      --ignore=lora/test_olmoe_tp.py \
-      --ignore=lora/test_deepseekv2_tp.py \
-      --ignore=lora/test_gptoss.py \
-      --ignore=lora/test_qwen3moe_tp.py
-      
+      --ignore=lora/test_llm_with_multi_loras.py
   parallelism: 4
 
 - label: PyTorch Compilation Unit Tests # 15min
@@ -433,8 +416,8 @@ steps:
   - pytest -v -s compile/test_basic_correctness.py
   - pytest -v -s compile/piecewise/
 
-- label: PyTorch Fullgraph Test # 22min
-  timeout_in_minutes: 35
+- label: PyTorch Fullgraph Test # 20min
+  timeout_in_minutes: 30
   mirror_hardwares: [amdexperimental]
   torch_nightly: true
   source_file_dependencies:
@@ -442,19 +425,6 @@ steps:
   - tests/compile
   commands:
   - pytest -v -s compile/test_full_graph.py
-  - pytest -v -s compile/test_fusions_e2e.py
-
-- label: Cudagraph test
-  timeout_in_minutes: 20
-  mirror_hardwares: [amdexperimental]
-  source_file_dependencies:
-  - tests/v1/cudagraph
-  - vllm/v1/cudagraph_dispatcher.py
-  - vllm/config/compilation.py
-  - vllm/compilation
-  commands:
-    - pytest -v -s v1/cudagraph/test_cudagraph_dispatch.py
-    - pytest -v -s v1/cudagraph/test_cudagraph_mode.py
 
 - label: Kernels Core Operation Test # 48min
   timeout_in_minutes: 75
@@ -558,8 +528,8 @@ steps:
   # https://github.com/pytorch/ao/issues/2919, we'll have to skip new torchao tests for now
   # we can only upgrade after this is resolved
   # TODO(jerryzh168): resolve the above comment
-  - uv pip install --system torchao==0.13.0 --index-url https://download.pytorch.org/whl/cu129
-  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py
+  - uv pip install --system torchao==0.13.0
+  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/
 
 - label: LM Eval Small Models # 53min
   timeout_in_minutes: 75
@@ -708,10 +678,8 @@ steps:
   - vllm/
   - tests/models/language/generation
   commands:
-    # Install fast path packages for testing against transformers
-    # Note: also needed to run plamo2 model in vLLM
-    - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.2.5'
-    - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
+    # Install causal-conv1d for plamo2 models here, as it is not compatible with pip-compile.
+    - pip install 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.0.post8'
     - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
 
 - label: Language Models Test (PPL)
@@ -839,8 +807,8 @@ steps:
     # Whisper needs spawn method to avoid deadlock
     - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
 
-- label: Blackwell Test # 21 min
-  timeout_in_minutes: 30
+- label: Blackwell Test # 38 min
+  timeout_in_minutes: 60
   working_dir: "/vllm-workspace/"
   gpu: b200
   # optional: true
@@ -853,6 +821,8 @@ steps:
   - vllm/model_executor/layers/fused_moe/flashinfer_cutlass_prepare_finalize.py
   - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
   - vllm/v1/attention/backends/flashinfer.py
+  - vllm/compilation/fusion.py
+  - vllm/compilation/fusion_attn.py
   commands:
     - nvidia-smi
     - python3 examples/offline_inference/basic/chat.py
@@ -869,32 +839,15 @@ steps:
     - pytest -v -s tests/kernels/quantization/test_nvfp4_scaled_mm.py
     - pytest -v -s tests/kernels/quantization/test_flashinfer_scaled_mm.py
     - pytest -v -s tests/kernels/quantization/test_flashinfer_nvfp4_scaled_mm.py
-    - pytest -v -s tests/kernels/quantization/test_nvfp4_qutlass.py
-    - pytest -v -s tests/kernels/quantization/test_mxfp4_qutlass.py
     - pytest -v -s tests/kernels/moe/test_nvfp4_moe.py
     - pytest -v -s tests/kernels/moe/test_ocp_mx_moe.py
-    - pytest -v -s tests/kernels/moe/test_flashinfer.py
-
-- label: Blackwell Fusion Tests # 30 min
-  timeout_in_minutes: 40
-  working_dir: "/vllm-workspace/"
-  gpu: b200
-  source_file_dependencies:
-  - csrc/quantization/fp4/
-  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
-  - vllm/v1/attention/backends/flashinfer.py
-  - vllm/compilation/
-  # can affect pattern matching
-  - vllm/model_executor/layers/layernorm.py
-  - vllm/model_executor/layers/activation.py
-  - vllm/model_executor/layers/quantization/input_quant_fp8.py
-  commands:
-    - nvidia-smi
-    - pytest -v -s tests/compile/test_fusion_attn.py
-    - pytest -v -s tests/compile/test_silu_mul_quant_fusion.py
-    # this runner has 2 GPUs available even though num_gpus=2 is not set
+    # Fusion
     - pytest -v -s tests/compile/test_fusion_all_reduce.py
-    - pytest -v -s tests/compile/test_fusions_e2e.py
+    - pytest -v -s tests/compile/test_fusion_attn.py::test_attention_quant_pattern
+    - pytest -v -s tests/kernels/moe/test_flashinfer.py
+    - pytest -v -s tests/compile/test_silu_mul_quant_fusion.py
+    - pytest -v -s tests/kernels/quantization/test_nvfp4_qutlass.py
+    - pytest -v -s tests/kernels/quantization/test_mxfp4_qutlass.py
 
 - label: Blackwell GPT-OSS Eval
   timeout_in_minutes: 60
@@ -1001,8 +954,6 @@ steps:
   - tests/v1/shutdown
   - tests/v1/worker/test_worker_memory_snapshot.py
   commands:
-  # https://github.com/NVIDIA/nccl/issues/1838
-  - export NCCL_CUMEM_HOST_ENABLE=0
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
   - DP_SIZE=2 pytest -v -s v1/entrypoints/openai/test_multi_api_servers.py
@@ -1010,7 +961,6 @@ steps:
   - pytest -v -s ./compile/test_basic_correctness.py
   - pytest -v -s ./compile/test_wrapper.py
   - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
-  - VLLM_TEST_SAME_HOST=1 VLLM_TEST_WITH_DEFAULT_DEVICE_SET=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
   - pytest -v -s distributed/test_sequence_parallel.py
   - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
   - pytest -v -s v1/worker/test_worker_memory_snapshot.py
@@ -1054,11 +1004,6 @@ steps:
   - pytest -v -s plugins_tests/test_io_processor_plugins.py
   - pip uninstall prithvi_io_processor_plugin -y
   # end io_processor plugins test
-  # begin stat_logger plugins test
-  - pip install -e ./plugins/vllm_add_dummy_stat_logger
-  - pytest -v -s plugins_tests/test_stats_logger_plugins.py
-  - pip uninstall dummy_stat_logger -y
-  # end stat_logger plugins test
   # other tests continue here:
   - pytest -v -s plugins_tests/test_scheduler_plugins.py
   - pip install -e ./plugins/vllm_add_dummy_model
@@ -1098,7 +1043,6 @@ steps:
     - pytest -v -s -x lora/test_chatglm3_tp.py
     - pytest -v -s -x lora/test_llama_tp.py
     - pytest -v -s -x lora/test_llm_with_multi_loras.py
-    - pytest -v -s -x lora/test_olmoe_tp.py
 
 
 - label: Weight Loading Multiple GPU Test  # 33min
@@ -1124,17 +1068,6 @@ steps:
   - tests/weight_loading
   commands:
     - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large.txt
-  
-- label: NixlConnector PD accuracy tests (Distributed) # 30min
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - bash v1/kv_connector/nixl_integration/tp_config_sweep_accuracy_test.sh
 
 
 ##### multi gpus test #####
@@ -1167,7 +1100,7 @@ steps:
   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
 
 ##### H200 test #####
-- label: Distributed Tests (H200) # optional
+- label: Distrubted Tests (H200) # optional
   gpu: h200
   optional: true
   working_dir: "/vllm-workspace/"
@@ -1175,8 +1108,6 @@ steps:
   commands:
     - pytest -v -s tests/compile/test_async_tp.py
     - pytest -v -s tests/compile/test_sequence_parallelism.py
-    - pytest -v -s tests/compile/test_fusion_all_reduce.py
-    - pytest -v -s tests/compile/test_fusions_e2e.py::test_tp2_attn_quant_allreduce_rmsnorm
     - pytest -v -s tests/distributed/test_context_parallel.py
     - CUDA_VISIBLE_DEVICES=1,2 VLLM_ALL2ALL_BACKEND=deepep_high_throughput VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model Qwen/Qwen1.5-MoE-A2.7B --tp-size=1  --dp-size=2 --max-model-len 2048
 

.github/CODEOWNERS

@@ -5,8 +5,8 @@
 /vllm/attention @LucasWilkinson
 /vllm/attention/backends/abstract.py @WoosukKwon @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
 /vllm/executor/executor_base.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill @22quinn
-/vllm/model_executor/layers/fused_moe @mgoin @pavanimajety
-/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256 @pavanimajety
+/vllm/model_executor/layers/fused_moe @mgoin
+/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256
 /vllm/model_executor/layers/mamba @tdoublep
 /vllm/model_executor/model_loader @22quinn
 /vllm/multimodal @DarkLight1337 @ywang96 @NickLucche
@@ -25,8 +25,7 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 
 # vLLM V1
 /vllm/v1/attention @LucasWilkinson
-/vllm/v1/attention/backends/mla @pavanimajety
-/vllm/v1/attention/backends/flashinfer.py @mgoin @pavanimajety
+/vllm/v1/attention/backends/flashinfer.py @mgoin
 /vllm/v1/attention/backends/triton_attn.py @tdoublep
 /vllm/v1/core @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @comaniac @alexm-redhat @heheda12345 @ApostaC
 /vllm/v1/sample @22quinn @houseroad @njhill
@@ -45,7 +44,7 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /tests/kernels @mgoin @tlrmchlsmth @WoosukKwon @yewentao256
 /tests/models @DarkLight1337 @ywang96
 /tests/multimodal @DarkLight1337 @ywang96 @NickLucche
-/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256 @pavanimajety
+/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256
 /tests/test_inputs.py @DarkLight1337 @ywang96
 /tests/v1/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb @aarnphm
 /tests/v1/structured_output @mgoin @russellb @aarnphm
@@ -58,7 +57,7 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /tests/v1/offloading @ApostaC
 
 # Transformers backend
-/vllm/model_executor/models/transformers @hmellor
+/vllm/model_executor/models/transformers.py @hmellor
 /tests/models/test_transformers.py @hmellor
 
 # Docs

.gitignore

@@ -94,9 +94,6 @@ ipython_config.py
 # generated files
 **/generated/**
 
-# uv
-uv.lock
-
 # pyenv
 #   For a library or package, you might want to ignore these files since the code is
 #   intended to run in multiple environments; otherwise, check them in:

.markdownlint.yaml

@@ -4,6 +4,7 @@ MD013: false
 MD024:
   siblings_only: true
 MD033: false
+MD042: false
 MD045: false
 MD046: false
 MD051: false

.pre-commit-config.yaml

@@ -38,7 +38,7 @@ repos:
   rev: 0.9.1
   hooks:
     - id: pip-compile
-      args: [requirements/test.in, -o, requirements/test.txt, --index-strategy, unsafe-best-match, --torch-backend, cu129, --python-platform, x86_64-manylinux_2_28]
+      args: [requirements/test.in, -o, requirements/test.txt, --index-strategy, unsafe-best-match, --torch-backend, cu128, --python-platform, x86_64-manylinux_2_28]
       files: ^requirements/test\.(in|txt)$
 - repo: local
   hooks:
@@ -48,8 +48,8 @@ repos:
     entry: python tools/generate_nightly_torch_test.py
     files: ^requirements/test\.(in|txt)$
   - id: mypy-local
-    name: Run mypy locally for lowest supported Python version
-    entry: python tools/pre_commit/mypy.py 0 "3.10"
+    name: Run mypy for local Python installation
+    entry: python tools/pre_commit/mypy.py 0 "local"
     stages: [pre-commit] # Don't run in CI
     <<: &mypy_common
       language: python

CMakeLists.txt

@@ -49,8 +49,8 @@ set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1
 # requirements.txt files and should be kept consistent.  The ROCm torch
 # versions are derived from docker/Dockerfile.rocm
 #
-set(TORCH_SUPPORTED_VERSION_CUDA "2.9.0")
-set(TORCH_SUPPORTED_VERSION_ROCM "2.9.0")
+set(TORCH_SUPPORTED_VERSION_CUDA "2.8.0")
+set(TORCH_SUPPORTED_VERSION_ROCM "2.8.0")
 
 #
 # Try to find python package with an executable that exactly matches
@@ -883,7 +883,6 @@ target_compile_definitions(_C PRIVATE CUTLASS_ENABLE_DIRECT_CUDA_DRIVER_CALL=1)
 set(VLLM_MOE_EXT_SRC
   "csrc/moe/torch_bindings.cpp"
   "csrc/moe/moe_align_sum_kernels.cu"
-  "csrc/moe/moe_lora_align_sum_kernels.cu"
   "csrc/moe/topk_softmax_kernels.cu")
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")

benchmarks/benchmark_block_pool.py

@@ -5,7 +5,7 @@ import gc
 from benchmark_utils import TimeCollector
 from tabulate import tabulate
 
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 from vllm.v1.core.block_pool import BlockPool
 
 

benchmarks/benchmark_long_document_qa_throughput.py

@@ -46,7 +46,7 @@ import time
 
 from vllm import LLM, SamplingParams
 from vllm.engine.arg_utils import EngineArgs
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 
 def test_long_document_qa(llm=None, sampling_params=None, prompts=None):

benchmarks/benchmark_ngram_proposer.py

@@ -19,7 +19,7 @@ from vllm.config import (
     VllmConfig,
 )
 from vllm.platforms import current_platform
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 from vllm.v1.spec_decode.ngram_proposer import NgramProposer
 from vllm.v1.worker.gpu_input_batch import InputBatch
 from vllm.v1.worker.gpu_model_runner import GPUModelRunner

benchmarks/benchmark_prefix_caching.py

@@ -37,7 +37,7 @@ from transformers import PreTrainedTokenizerBase
 
 from vllm import LLM, SamplingParams
 from vllm.engine.arg_utils import EngineArgs
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 try:
     from vllm.transformers_utils.tokenizer import get_tokenizer

benchmarks/benchmark_prioritization.py

@@ -11,7 +11,7 @@ import time
 from transformers import AutoTokenizer, PreTrainedTokenizerBase
 
 from vllm.engine.arg_utils import EngineArgs
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 
 # Select a equi-probable random priority

benchmarks/benchmark_serving_structured_output.py

@@ -31,7 +31,6 @@ import time
 import uuid
 import warnings
 from collections.abc import AsyncGenerator
-from contextlib import nullcontext
 from dataclasses import dataclass
 
 import datasets
@@ -51,7 +50,7 @@ except ImportError:
     from backend_request_func import get_tokenizer
 
 try:
-    from vllm.utils.argparse_utils import FlexibleArgumentParser
+    from vllm.utils import FlexibleArgumentParser
 except ImportError:
     from argparse import ArgumentParser as FlexibleArgumentParser
 
@@ -502,9 +501,15 @@ async def benchmark(
 
     pbar = None if disable_tqdm else tqdm(total=len(input_requests))
 
-    semaphore = asyncio.Semaphore(max_concurrency) if max_concurrency else nullcontext()
+    # This can be used once the minimum Python version is 3.10 or higher,
+    # and it will simplify the code in limited_request_func.
+    #    semaphore = (asyncio.Semaphore(max_concurrency)
+    #                 if max_concurrency else contextlib.nullcontext())
+    semaphore = asyncio.Semaphore(max_concurrency) if max_concurrency else None
 
     async def limited_request_func(request_func_input, pbar):
+        if semaphore is None:
+            return await request_func(request_func_input=request_func_input, pbar=pbar)
         async with semaphore:
             return await request_func(request_func_input=request_func_input, pbar=pbar)
 

benchmarks/cutlass_benchmarks/sparse_benchmarks.py

@@ -15,7 +15,7 @@ from utils import make_rand_sparse_tensors
 from weight_shapes import WEIGHT_SHAPES
 
 from vllm import _custom_ops as ops
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
 DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512]

benchmarks/cutlass_benchmarks/w8a8_benchmarks.py

@@ -18,8 +18,7 @@ from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     w8a8_triton_block_scaled_mm,
 )
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.math_utils import cdiv
+from vllm.utils import FlexibleArgumentParser, cdiv
 
 DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
 DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512]

benchmarks/kernels/bench_per_token_quant_fp8.py

@@ -10,8 +10,7 @@ import torch
 from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
 from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
 from vllm.triton_utils import triton
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
 
 
 def with_triton_mode(fn):

benchmarks/kernels/benchmark_activation.py

@@ -10,8 +10,7 @@ import vllm.model_executor.layers.activation  # noqa F401
 from vllm.model_executor.custom_op import CustomOp
 from vllm.platforms import current_platform
 from vllm.triton_utils import triton
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
 
 batch_size_range = [1, 16, 32, 64, 128]
 seq_len_range = [1, 16, 64, 128, 256, 512, 1024, 2048, 4096]

benchmarks/kernels/benchmark_bitblas.py

@@ -28,7 +28,7 @@ except ImportError as e:
 
 from bitblas import Matmul, MatmulConfig, auto_detect_nvidia_target
 
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 parser = FlexibleArgumentParser(
     description="Benchmark BitBLAS int4 on a specific target."

benchmarks/kernels/benchmark_cutlass_fp4_moe.py

@@ -20,7 +20,7 @@ from vllm.model_executor.layers.fused_moe.config import (
 from vllm.model_executor.layers.fused_moe.cutlass_moe import cutlass_moe_fp4
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
 from vllm.scalar_type import scalar_types
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 WEIGHT_SHAPES_MOE = {
     "nvidia/DeepSeek-R1-FP4": [

benchmarks/kernels/benchmark_cutlass_moe_fp8.py

@@ -14,7 +14,7 @@ from vllm.model_executor.layers.fused_moe.config import fp8_w8a8_moe_quant_confi
 from vllm.model_executor.layers.fused_moe.cutlass_moe import cutlass_moe_fp8
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
 from vllm.platforms import current_platform
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 # Weight shapes for different models: [num_experts, topk, hidden_size,
 # intermediate_size]

benchmarks/kernels/benchmark_device_communicators.py

@@ -39,7 +39,7 @@ from vllm.distributed.device_communicators.pynccl_allocator import (
 )
 from vllm.distributed.device_communicators.symm_mem import SymmMemCommunicator
 from vllm.logger import init_logger
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 logger = init_logger(__name__)
 

benchmarks/kernels/benchmark_grouped_gemm_cutlass.py

@@ -13,7 +13,7 @@ from vllm.model_executor.layers.fused_moe.fused_moe import (
     fused_experts,
     fused_topk,
 )
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = [
     "nm-testing/Mixtral-8x7B-Instruct-v0.1",

benchmarks/kernels/benchmark_layernorm.py

@@ -7,8 +7,7 @@ import torch
 
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.platforms import current_platform
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
 
 
 @torch.inference_mode()

benchmarks/kernels/benchmark_lora.py

@@ -25,7 +25,7 @@ if HAS_TRITON:
     from vllm.lora.ops.triton_ops import LoRAKernelMeta, lora_expand, lora_shrink
     from vllm.lora.ops.triton_ops.utils import _LORA_A_PTR_DICT, _LORA_B_PTR_DICT
 
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
 DEFAULT_TP_SIZES = [1]

benchmarks/kernels/benchmark_machete.py

@@ -33,7 +33,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     quantize_weights,
 )
 from vllm.scalar_type import ScalarType, scalar_types
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = ["meta-llama/Llama-3-8b", "meta-llama/Llama-2-70b-hf"]
 DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512, 1024]

benchmarks/kernels/benchmark_marlin.py

@@ -44,7 +44,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     sort_weights,
 )
 from vllm.scalar_type import ScalarType, scalar_types
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = ["meta-llama/Llama-2-7b-hf/TP1"]
 DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192]

benchmarks/kernels/benchmark_moe.py

@@ -22,7 +22,7 @@ from vllm.model_executor.layers.fused_moe.fused_moe import *
 from vllm.platforms import current_platform
 from vllm.transformers_utils.config import get_config
 from vllm.triton_utils import triton
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 FP8_DTYPE = current_platform.fp8_dtype()
 

benchmarks/kernels/benchmark_moe_permute_unpermute.py

@@ -17,7 +17,7 @@ from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import (
 )
 from vllm.model_executor.layers.fused_moe.utils import _fp8_quantize
 from vllm.platforms import current_platform
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 FP8_DTYPE = current_platform.fp8_dtype()
 

benchmarks/kernels/benchmark_mrope.py

@@ -39,7 +39,7 @@ import torch
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.platforms import current_platform
 from vllm.transformers_utils.config import get_config
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 

benchmarks/kernels/benchmark_paged_attention.py

@@ -9,9 +9,9 @@ import torch
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import (
+from vllm.utils import (
     STR_DTYPE_TO_TORCH_DTYPE,
+    FlexibleArgumentParser,
     create_kv_caches_with_random,
 )
 

benchmarks/kernels/benchmark_polynorm.py

@@ -0,0 +1,155 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import itertools
+
+import torch
+
+from vllm import _custom_ops as vllm_ops
+from vllm.triton_utils import triton
+
+
+def polynorm_naive(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    bias: torch.Tensor,
+    eps: float = 1e-6,
+):
+    orig_shape = x.shape
+    x = x.view(-1, x.shape[-1])
+
+    def norm(x, eps: float):
+        return x / torch.sqrt(x.pow(2).mean(-1, keepdim=True) + eps)
+
+    x = x.float()
+    return (
+        (
+            weight[0] * norm(x**3, eps)
+            + weight[1] * norm(x**2, eps)
+            + weight[2] * norm(x, eps)
+            + bias
+        )
+        .to(weight.dtype)
+        .view(orig_shape)
+    )
+
+
+def polynorm_vllm(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    bias: torch.Tensor,
+    eps: float = 1e-6,
+):
+    orig_shape = x.shape
+    x = x.view(-1, x.shape[-1])
+
+    out = torch.empty_like(x)
+    vllm_ops.poly_norm(out, x, weight, bias, eps)
+    output = out
+
+    output = output.view(orig_shape)
+    return output
+
+
+def calculate_diff(batch_size, seq_len, hidden_dim):
+    dtype = torch.bfloat16
+    x = torch.randn(batch_size, seq_len, hidden_dim, dtype=dtype, device="cuda")
+    weight = torch.ones(3, dtype=dtype, device="cuda")
+    bias = torch.ones(1, dtype=dtype, device="cuda")
+
+    output_naive = polynorm_naive(x, weight, bias)
+    output_vllm = polynorm_vllm(x, weight, bias)
+
+    if torch.allclose(output_naive, output_vllm, atol=1e-2, rtol=1e-2):
+        print("✅ All implementations match")
+    else:
+        print("❌ Implementations differ")
+
+
+batch_size_range = [2**i for i in range(0, 7, 2)]
+seq_length_range = [2**i for i in range(6, 11, 1)]
+dim_range = [2048, 4096]
+configs = list(itertools.product(dim_range, batch_size_range, seq_length_range))
+
+
+def get_benchmark():
+    @triton.testing.perf_report(
+        triton.testing.Benchmark(
+            x_names=["dim", "batch_size", "seq_len"],
+            x_vals=[list(_) for _ in configs],
+            line_arg="provider",
+            line_vals=["naive", "vllm"],
+            line_names=["Naive", "vLLM"],
+            styles=[("blue", "-"), ("red", "-")],
+            ylabel="us",
+            plot_name="polynorm-perf",
+            args={},
+        )
+    )
+    def benchmark(dim, batch_size, seq_len, provider):
+        dtype = torch.bfloat16
+        hidden_dim = dim * 4
+
+        x = torch.randn(batch_size, seq_len, hidden_dim, dtype=dtype, device="cuda")
+        weight = torch.ones(3, dtype=dtype, device="cuda")
+        bias = torch.ones(1, dtype=dtype, device="cuda")
+
+        quantiles = [0.5, 0.2, 0.8]
+
+        if provider == "naive":
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                lambda: polynorm_naive(x, weight, bias),
+                quantiles=quantiles,
+            )
+        else:
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                lambda: polynorm_vllm(x, weight, bias),
+                quantiles=quantiles,
+            )
+
+        return 1000 * ms, 1000 * max_ms, 1000 * min_ms
+
+    return benchmark
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=4,
+        help="Batch size",
+    )
+    parser.add_argument(
+        "--seq-len",
+        type=int,
+        default=128,
+        help="Sequence length",
+    )
+    parser.add_argument(
+        "--hidden-dim",
+        type=int,
+        default=8192,
+        help="Intermediate size of MLP",
+    )
+    parser.add_argument(
+        "--save-path",
+        type=str,
+        default="./configs/polnorm/",
+        help="Path to save polnorm benchmark results",
+    )
+
+    args = parser.parse_args()
+
+    # Run correctness test
+    calculate_diff(
+        batch_size=args.batch_size,
+        seq_len=args.seq_len,
+        hidden_dim=args.hidden_dim,
+    )
+
+    benchmark = get_benchmark()
+    # Run performance benchmark
+    benchmark.run(print_data=True, save_path=args.save_path)

benchmarks/kernels/benchmark_quant.py

@@ -7,8 +7,7 @@ import torch
 
 from vllm import _custom_ops as ops
 from vllm.platforms import current_platform
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
 
 
 @torch.inference_mode()

benchmarks/kernels/benchmark_reshape_and_cache.py

@@ -9,9 +9,9 @@ from tabulate import tabulate
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import (
+from vllm.utils import (
     STR_DTYPE_TO_TORCH_DTYPE,
+    FlexibleArgumentParser,
     create_kv_caches_with_random,
 )
 

benchmarks/kernels/benchmark_reshape_and_cache_flash.py

@@ -12,9 +12,9 @@ from vllm.attention.ops.triton_reshape_and_cache_flash import (
 )
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import (
+from vllm.utils import (
     STR_DTYPE_TO_TORCH_DTYPE,
+    FlexibleArgumentParser,
     create_kv_caches_with_random_flash,
 )
 

benchmarks/kernels/benchmark_rope.py

@@ -8,7 +8,7 @@ import torch
 
 from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding, get_rope
 from vllm.platforms import current_platform
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 
 def benchmark_rope_kernels_multi_lora(

benchmarks/kernels/benchmark_trtllm_decode_attention.py

@@ -8,7 +8,7 @@ from datetime import datetime
 import flashinfer
 import torch
 
-from vllm.utils.math_utils import round_up
+from vllm.utils import round_up
 
 FLOAT32_BYTES = torch.finfo(torch.float).bits // 8
 FP8_DTYPE = torch.float8_e4m3fn

benchmarks/kernels/benchmark_trtllm_prefill_attention.py

@@ -8,7 +8,7 @@ from datetime import datetime
 import flashinfer
 import torch
 
-from vllm.utils.math_utils import round_up
+from vllm.utils import round_up
 
 FLOAT32_BYTES = torch.finfo(torch.float).bits // 8
 FP8_DTYPE = torch.float8_e4m3fn

benchmarks/kernels/benchmark_w8a8_block_fp8.py

@@ -18,7 +18,7 @@ from vllm.model_executor.layers.quantization.utils.fp8_utils import (
 )
 from vllm.platforms import current_platform
 from vllm.triton_utils import triton
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 mp.set_start_method("spawn", force=True)
 

benchmarks/kernels/graph_machete_bench.py

@@ -11,7 +11,7 @@ import regex as re
 import seaborn as sns
 from torch.utils.benchmark import Measurement as TMeasurement
 
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 if __name__ == "__main__":
     parser = FlexibleArgumentParser(

benchmarks/multi_turn/benchmark_serving_multi_turn.py

@@ -1251,7 +1251,7 @@ async def main() -> None:
         default=None,
         help="The model name used in the API. "
         "If not specified, the model name will be the "
-        "same as the `--model` argument. ",
+        "same as the ``--model`` argument. ",
     )
 
     parser.add_argument(

benchmarks/overheads/benchmark_hashing.py

@@ -5,7 +5,7 @@ import cProfile
 import pstats
 
 from vllm import LLM, SamplingParams
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 # A very long prompt, total number of tokens is about 15k.
 LONG_PROMPT = ["You are an expert in large language models, aren't you?"] * 1000

cmake/cpu_extension.cmake

@@ -188,60 +188,16 @@ else()
     message(FATAL_ERROR "vLLM CPU backend requires AVX512, AVX2, Power9+ ISA, S390X ISA, ARMv8 or RISC-V support.")
 endif()
 
+#
+# Build oneDNN for W8A8 GEMM kernels (only for x86-AVX512 /ARM platforms)
+# Flag to enable ACL kernels for AARCH64 platforms
+if (VLLM_BUILD_ACL STREQUAL "ON")
+    set(USE_ACL ON)
+else()
+    set(USE_ACL OFF)
+endif()
 
-# Build oneDNN for GEMM kernels (only for x86-AVX512 /ARM platforms)
 if ((AVX512_FOUND AND NOT AVX512_DISABLED) OR (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND) OR POWER9_FOUND OR POWER10_FOUND OR POWER11_FOUND)
-    # Fetch and build Arm Compute Library (ACL) as oneDNN's backend for AArch64
-    # TODO [fadara01]: remove this once ACL can be fetched and built automatically as a dependency of oneDNN
-    if(ASIMD_FOUND)
-        if(DEFINED ENV{ACL_ROOT_DIR} AND IS_DIRECTORY "$ENV{ACL_ROOT_DIR}")
-            message(STATUS "Using ACL from specified source directory: $ENV{ACL_ROOT_DIR}")
-        else()
-            message(STATUS "Downloading Arm Compute Library (ACL) from GitHub")
-            FetchContent_Populate(arm_compute
-                SUBBUILD_DIR "${FETCHCONTENT_BASE_DIR}/arm_compute-subbuild"
-                SOURCE_DIR   "${FETCHCONTENT_BASE_DIR}/arm_compute-src"
-                GIT_REPOSITORY https://github.com/ARM-software/ComputeLibrary.git
-                GIT_TAG        v52.2.0
-                GIT_SHALLOW    TRUE
-                GIT_PROGRESS   TRUE
-            )
-            set(ENV{ACL_ROOT_DIR} "${arm_compute_SOURCE_DIR}")
-        endif()
-
-        # Build ACL with scons
-        include(ProcessorCount)
-        ProcessorCount(_NPROC)
-        set(_scons_cmd
-        scons -j${_NPROC}
-            Werror=0 debug=0 neon=1 examples=0 embed_kernels=0 os=linux
-            arch=armv8.2-a build=native benchmark_examples=0 fixed_format_kernels=1
-            multi_isa=1 openmp=1 cppthreads=0
-        )
-
-        # locate PyTorch's libgomp (e.g. site-packages/torch.libs/libgomp-947d5fa1.so.1.0.0)
-        # and create a local shim dir with it
-        include("${CMAKE_CURRENT_LIST_DIR}/utils.cmake")
-        vllm_prepare_torch_gomp_shim(VLLM_TORCH_GOMP_SHIM_DIR)
-
-        if(NOT VLLM_TORCH_GOMP_SHIM_DIR STREQUAL "")
-            list(APPEND _scons_cmd extra_link_flags=-L${VLLM_TORCH_GOMP_SHIM_DIR})
-        endif()
-
-        execute_process(
-            COMMAND ${_scons_cmd}
-            WORKING_DIRECTORY "$ENV{ACL_ROOT_DIR}"
-            RESULT_VARIABLE _acl_rc
-        )
-        if(NOT _acl_rc EQUAL 0)
-            message(FATAL_ERROR "ACL SCons build failed (exit ${_acl_rc}).")
-        endif()
-
-        set(ONEDNN_AARCH64_USE_ACL "ON")
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wl,-rpath,$ENV{ACL_ROOT_DIR}/build/")
-        add_compile_definitions(VLLM_USE_ACL)
-    endif()
-
     set(FETCHCONTENT_SOURCE_DIR_ONEDNN "$ENV{FETCHCONTENT_SOURCE_DIR_ONEDNN}" CACHE PATH "Path to a local oneDNN source directory.")
 
     if(FETCHCONTENT_SOURCE_DIR_ONEDNN)
@@ -261,6 +217,16 @@ if ((AVX512_FOUND AND NOT AVX512_DISABLED) OR (ASIMD_FOUND AND NOT APPLE_SILICON
         )
     endif()
 
+    if(USE_ACL)
+        find_library(ARM_COMPUTE_LIBRARY NAMES arm_compute PATHS $ENV{ACL_ROOT_DIR}/build/)
+        if(NOT ARM_COMPUTE_LIBRARY)
+            message(FATAL_ERROR "Could not find ARM Compute Library: please set ACL_ROOT_DIR")
+        endif()
+        set(ONEDNN_AARCH64_USE_ACL "ON")
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wl,-rpath,$ENV{ACL_ROOT_DIR}/build/")
+        add_compile_definitions(VLLM_USE_ACL)
+    endif()
+
     set(ONEDNN_LIBRARY_TYPE "STATIC")
     set(ONEDNN_BUILD_DOC "OFF")
     set(ONEDNN_BUILD_EXAMPLES "OFF")

cmake/external_projects/flashmla.cmake

@@ -19,7 +19,7 @@ else()
   FetchContent_Declare(
         flashmla
         GIT_REPOSITORY https://github.com/vllm-project/FlashMLA
-        GIT_TAG 46d64a8ebef03fa50b4ae74937276a5c940e3f95
+        GIT_TAG 5f65b85703c7ed75fda01e06495077caad207c3f
         GIT_PROGRESS TRUE
         CONFIGURE_COMMAND ""
         BUILD_COMMAND ""
@@ -66,7 +66,6 @@ if(FLASH_MLA_ARCHS)
         ${flashmla_SOURCE_DIR}/csrc/extension/torch_api.cpp
         ${flashmla_SOURCE_DIR}/csrc/extension/sm90/dense_fp8/pybind.cpp
         ${flashmla_SOURCE_DIR}/csrc/extension/sm90/dense_fp8/flash_fwd_mla_fp8_sm90.cu
-        ${flashmla_SOURCE_DIR}/csrc/extension/sm90/dense_fp8/flash_fwd_mla_metadata.cu
     )
 
     set(FlashMLA_INCLUDES

cmake/external_projects/vllm_flash_attn.cmake

@@ -38,7 +38,7 @@ else()
   FetchContent_Declare(
           vllm-flash-attn
           GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG a893712401d70362fbb299cd9c4b3476e8e9ed54
+          GIT_TAG 8f468e7da54a8e2f98abfa7c38636aac91c0cba1
           GIT_PROGRESS TRUE
           # Don't share the vllm-flash-attn build between build types
           BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn

cmake/utils.cmake

@@ -129,44 +129,6 @@ function (get_torch_gpu_compiler_flags OUT_GPU_FLAGS GPU_LANG)
   set(${OUT_GPU_FLAGS} ${GPU_FLAGS} PARENT_SCOPE)
 endfunction()
 
-# Find libgomp that gets shipped with PyTorch wheel and create a shim dir with:
-#   libgomp.so    -> libgomp-<hash>.so...
-#   libgomp.so.1  -> libgomp-<hash>.so...
-# OUTPUT: TORCH_GOMP_SHIM_DIR  ("" if not found)
-function(vllm_prepare_torch_gomp_shim TORCH_GOMP_SHIM_DIR)
-  set(${TORCH_GOMP_SHIM_DIR} "" PARENT_SCOPE)
-
-  # Use run_python to locate vendored libgomp; never throw on failure.
-  run_python(_VLLM_TORCH_GOMP_PATH
-    "
-import os, glob
-try:
-  import torch
-  torch_pkg = os.path.dirname(torch.__file__)
-  site_root = os.path.dirname(torch_pkg)
-  torch_libs = os.path.join(site_root, 'torch.libs')
-  print(glob.glob(os.path.join(torch_libs, 'libgomp-*.so*'))[0])
-except:
-  print('')
-"
-    "failed to probe torch.libs for libgomp")
-
-  if(_VLLM_TORCH_GOMP_PATH STREQUAL "" OR NOT EXISTS "${_VLLM_TORCH_GOMP_PATH}")
-    return()
-  endif()
-
-  # Create shim under the build tree
-  set(_shim "${CMAKE_BINARY_DIR}/gomp_shim")
-  file(MAKE_DIRECTORY "${_shim}")
-
-  execute_process(COMMAND ${CMAKE_COMMAND} -E rm -f "${_shim}/libgomp.so")
-  execute_process(COMMAND ${CMAKE_COMMAND} -E rm -f "${_shim}/libgomp.so.1")
-  execute_process(COMMAND ${CMAKE_COMMAND} -E create_symlink "${_VLLM_TORCH_GOMP_PATH}" "${_shim}/libgomp.so")
-  execute_process(COMMAND ${CMAKE_COMMAND} -E create_symlink "${_VLLM_TORCH_GOMP_PATH}" "${_shim}/libgomp.so.1")
-
-  set(${TORCH_GOMP_SHIM_DIR} "${_shim}" PARENT_SCOPE)
-endfunction()
-
 # Macro for converting a `gencode` version number to a cmake version number.
 macro(string_to_ver OUT_VER IN_STR)
   string(REGEX REPLACE "\([0-9]+\)\([0-9]\)" "\\1.\\2" ${OUT_VER} ${IN_STR})

csrc/attention/mla/cutlass_sm100_mla/device/sm100_mla.hpp

@@ -125,37 +125,32 @@ public:
   }
 
   static void set_split_kv (KernelArguments& args) {
+    // printf("set_split_kv start");
     if (args.split_kv >= 1) return;
     auto [H, K, D, B] = args.problem_shape;
+    // std::cout << H << " " << K << " " << D << " " << B << "\n";      
     int sm_count = args.hw_info.sm_count;
-    float seq_length_k = static_cast<float>(K) / 1024.0f;
-    int max_splits = 1;
+    // printf("    sm_count = %d\n", sm_count);
+    int max_splits = ceil_div(K, 128);
+    max_splits = min(16, max_splits);
 
-    if (B <= 4 && seq_length_k >= 16) {
-      max_splits = 16;
+    // TODO: This avoids a hang when the batch size larger than 1 and 
+    // there is more than 1 kv_splits. 
+    // Discuss with NVIDIA how this can be fixed.
+    if (B > 1) {
+      max_splits = min(1, max_splits);
     }
-    else if (B <= 8 && seq_length_k >= 4) {
-      max_splits = 8;
-    }
-    else if ((B <= 16 && seq_length_k >= 8) ||
-             (B == 48 && seq_length_k >= 32)) {
-      max_splits = 4;
-    }
-    else if ((B <= 32 && seq_length_k >= 16) ||
-             (B == 96 && seq_length_k >= 16)) {
-      max_splits = 2;
-    }
-    else {
-      max_splits = 1;
-    }
-
-    // Wave-aware scheduling: ensure integer number of waves in K dimension
+    
+    // printf("    max_splits = %d\n", max_splits);
     int sms_per_batch = max(1, sm_count / B);
+    // printf("    sms_per_batch = %d\n", sms_per_batch);
     int split_heur = min(max_splits, sms_per_batch);
     int waves = ceil_div(B * split_heur, sm_count);
     int k_waves = ceil_div(max_splits, split_heur);
     int split_wave_aware = ceil_div(max_splits, k_waves);
     args.split_kv = split_wave_aware;
+    // printf("    args.split_kv = %d\n", args.split_kv);
+
   }
 
   /// Determines whether the GEMM can execute the given problem.

csrc/core/batch_invariant.hpp

@@ -5,11 +5,11 @@
 
 namespace vllm {
 
-// vllm_is_batch_invariant(); returns true
-// if env VLLM_BATCH_INVARIANT=1
-inline bool vllm_is_batch_invariant() {
+// vllm_kernel_override_batch_invariant(); returns true
+// if env VLLM_KERNEL_OVERRIDE_BATCH_INVARIANT=1
+inline bool vllm_kernel_override_batch_invariant() {
   static bool cached = []() {
-    std::string env_key = "VLLM_BATCH_INVARIANT";
+    std::string env_key = "VLLM_KERNEL_OVERRIDE_BATCH_INVARIANT";
     const char* val = std::getenv(env_key.c_str());
     return (val && std::atoi(val) != 0) ? 1 : 0;
   }();

csrc/cpu/dnnl_helper.cpp

@@ -187,8 +187,7 @@ template <>
 struct hash<MatMulPrimitiveHandler::ClassMatmulCacheKey> {
   size_t operator()(
       const MatMulPrimitiveHandler::ClassMatmulCacheKey& val) const {
-    return hash<dnnl_dim_t>()(val.b_n_size) ^ hash<dnnl_dim_t>()(val.b_k_size) ^
-           hash<int>()(static_cast<int>(val.b_type));
+    return hash<dnnl_dim_t>()(val.b_n_size) ^ hash<dnnl_dim_t>()(val.b_k_size);
   }
 };
 
@@ -217,8 +216,7 @@ bool operator==(const W8A8MatMulPrimitiveHandler::MSizeCacheKey& l,
 
 bool operator==(const MatMulPrimitiveHandler::ClassMatmulCacheKey& l,
                 const MatMulPrimitiveHandler::ClassMatmulCacheKey& r) {
-  return l.b_n_size == r.b_n_size && l.b_k_size == r.b_k_size &&
-         l.b_type == r.b_type;
+  return l.b_n_size == r.b_n_size && l.b_k_size == r.b_k_size;
 }
 
 bool operator==(const MatMulPrimitiveHandler::MSizeCacheKey& l,
@@ -495,10 +493,8 @@ void MatMulPrimitiveHandler::execute(ExecArgs& args) {
 dnnl::matmul MatMulPrimitiveHandler::get_matmul_cache(
     const MSizeCacheKey& key) {
   if (m_size_cache_.get() == nullptr) {
-    ClassMatmulCacheKey class_key = {
-        .b_n_size = b_n_size_, .b_k_size = b_k_size_, .b_type = b_type_};
-    m_size_cache_ =
-        get_matul_class_primitive_cache(class_key, primitive_cache_size_);
+    ClassMatmulCacheKey key = {.b_n_size = b_n_size_, .b_k_size = b_k_size_};
+    m_size_cache_ = get_matul_class_primitive_cache(key, primitive_cache_size_);
   }
   return m_size_cache_->get_or_create(key, [&]() {
     dnnl::matmul::primitive_desc desc = this->create_primitive_desc(key, false);

csrc/cpu/dnnl_helper.h

@@ -199,7 +199,6 @@ class MatMulPrimitiveHandler : public DNNLMatMulPrimitiveHandler {
   struct ClassMatmulCacheKey {
     dnnl_dim_t b_n_size;
     dnnl_dim_t b_k_size;
-    dnnl::memory::data_type b_type;
 
     friend bool operator==(const ClassMatmulCacheKey& l,
                            const ClassMatmulCacheKey& r);

csrc/layernorm_kernels.cu

@@ -148,6 +148,211 @@ fused_add_rms_norm_kernel(
   }
 }
 
+/* Function specialization in the case of FP16/BF16 tensors.
+   Additional optimizations we can make in this case are
+   packed and vectorized operations, which help with the
+   memory latency bottleneck.
+
+   _f16VecPN struct extends _f16Vec to add operations specifically required for
+   polynomial normalization (poly norm).
+   The original _f16Vec does not include the sum-of-powers computation or
+   in-place polynomial normalization logic. */
+template <typename scalar_t, int width>
+struct alignas(16) _f16VecPN : _f16Vec<scalar_t, width> {
+  using Base = _f16Vec<scalar_t, width>;
+  using Converter = typename Base::Converter;
+  using T1 = typename Base::T1;
+  using T2 = typename Base::T2;
+  using Base::data;
+
+  __device__ auto sum_pows() const {
+    float s2 = 0.0f, s4 = 0.0f, s6 = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < width; i += 2) {
+      float2 z = Converter::convert(T2{data[i], data[i + 1]});
+      float x2 = z.x * z.x;
+      float x4 = x2 * x2;
+      float x6 = x4 * x2;
+
+      float y2 = z.y * z.y;
+      float y4 = y2 * y2;
+      float y6 = y4 * y2;
+
+      s2 += x2 + y2;
+      s4 += x4 + y4;
+      s6 += x6 + y6;
+    }
+    return std::make_tuple(s2, s4, s6);
+  }
+
+  __device__ void poly_norm_inplace(const float w2_inv_std,
+                                    const float w1_inv_std2,
+                                    const float w0_inv_std3, const float bias) {
+#pragma unroll
+    for (int i = 0; i < width; i += 2) {
+      float2 z = Converter::convert(T2{data[i], data[i + 1]});
+
+      float x2 = z.x * z.x;
+      float x3 = x2 * z.x;
+      z.x = w2_inv_std * z.x + w1_inv_std2 * x2 + w0_inv_std3 * x3 + bias;
+
+      float y2 = z.y * z.y;
+      float y3 = y2 * z.y;
+      z.y = w2_inv_std * z.y + w1_inv_std2 * y2 + w0_inv_std3 * y3 + bias;
+
+      auto out = Converter::convert(z);
+      data[i] = out.x;
+      data[i + 1] = out.y;
+    }
+  }
+};
+
+template <typename scalar_t, int width>
+__global__ std::enable_if_t<(width > 0) && _typeConvert<scalar_t>::exists>
+poly_norm_kernel(scalar_t* __restrict__ out,           // [..., hidden_size]
+                 const scalar_t* __restrict__ input,   // [..., hidden_size]
+                 const scalar_t* __restrict__ weight,  // [3]
+                 const scalar_t* __restrict__ bias,    // [1]
+                 const float epsilon, const int hidden_size) {
+  // Sanity checks on our vector struct and type-punned pointer arithmetic
+  static_assert(std::is_pod_v<_f16VecPN<scalar_t, width>>);
+  static_assert(sizeof(_f16VecPN<scalar_t, width>) == sizeof(scalar_t) * width);
+
+  /* These and the argument pointers are all declared `restrict` as they are
+     not aliased in practice. Argument pointers should not be dereferenced
+     in this kernel as that would be undefined behavior */
+  auto* __restrict__ input_v =
+      reinterpret_cast<const _f16VecPN<scalar_t, width>*>(input);
+  const int vec_hidden_size = hidden_size / width;
+  float variance = 0.0f;
+  float variance2 = 0.0f;
+  float variance3 = 0.0f;
+
+  for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x) {
+    int id = blockIdx.x * vec_hidden_size + idx;
+    _f16VecPN<scalar_t, width> temp = input_v[id];
+    auto [x2, x4, x6] = temp.sum_pows();
+
+    variance += x2;
+    variance2 += x4;
+    variance3 += x6;
+  }
+
+  float3 thread_variances = make_float3(variance, variance2, variance3);
+
+  struct SumOp {
+    __device__ float3 operator()(const float3& a, const float3& b) const {
+      return make_float3(a.x + b.x, a.y + b.y, a.z + b.z);
+    }
+  };
+
+  using BlockReduce = cub::BlockReduce<float3, 1024>;
+  __shared__ typename BlockReduce::TempStorage reduceStore;
+  float3 block_variances =
+      BlockReduce(reduceStore).Reduce(thread_variances, SumOp{}, blockDim.x);
+
+  variance = block_variances.x;
+  variance2 = block_variances.y;
+  variance3 = block_variances.z;
+
+  __shared__ float s_w2_inv_std;
+  __shared__ float s_w1_inv_std2;
+  __shared__ float s_w0_inv_std3;
+  __shared__ float s_bias;
+
+  if (threadIdx.x == 0) {
+    float w0 = (float)weight[0];
+    float w1 = (float)weight[1];
+    float w2 = (float)weight[2];
+    s_bias = (float)bias[0];
+
+    s_w2_inv_std = w2 * rsqrtf(variance / hidden_size + epsilon);
+    s_w1_inv_std2 = w1 * rsqrtf(variance2 / hidden_size + epsilon);
+    s_w0_inv_std3 = w0 * rsqrtf(variance3 / hidden_size + epsilon);
+  }
+  __syncthreads();
+
+  auto* __restrict__ out_v = reinterpret_cast<_f16VecPN<scalar_t, width>*>(out);
+
+  for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x) {
+    int id = blockIdx.x * vec_hidden_size + idx;
+    _f16VecPN<scalar_t, width> temp = input_v[id];
+    temp.poly_norm_inplace(s_w2_inv_std, s_w1_inv_std2, s_w0_inv_std3, s_bias);
+    out_v[id] = temp;
+  }
+}
+
+/* Generic poly_norm_kernel
+   The width field is not used here but necessary for other specializations.
+ */
+template <typename scalar_t, int width>
+__global__ std::enable_if_t<(width == 0) || !_typeConvert<scalar_t>::exists>
+poly_norm_kernel(scalar_t* __restrict__ out,           // [..., hidden_size]
+                 const scalar_t* __restrict__ input,   // [..., hidden_size]
+                 const scalar_t* __restrict__ weight,  // [3]
+                 const scalar_t* __restrict__ bias,    // [1]
+                 const float epsilon, const int hidden_size) {
+  float variance = 0.0f;
+  float variance2 = 0.0f;
+  float variance3 = 0.0f;
+
+  for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
+    float x = (float)input[blockIdx.x * hidden_size + idx];
+    float x2 = x * x;
+    float x4 = x2 * x2;
+    float x6 = x4 * x2;
+
+    variance += x2;
+    variance2 += x4;
+    variance3 += x6;
+  }
+
+  float3 thread_variances = make_float3(variance, variance2, variance3);
+
+  struct SumOp {
+    __device__ float3 operator()(const float3& a, const float3& b) const {
+      return make_float3(a.x + b.x, a.y + b.y, a.z + b.z);
+    }
+  };
+
+  using BlockReduce = cub::BlockReduce<float3, 1024>;
+  __shared__ typename BlockReduce::TempStorage reduceStore;
+  float3 block_variances =
+      BlockReduce(reduceStore).Reduce(thread_variances, SumOp{}, blockDim.x);
+
+  variance = block_variances.x;
+  variance2 = block_variances.y;
+  variance3 = block_variances.z;
+
+  __shared__ float s_w2_inv_std;
+  __shared__ float s_w1_inv_std2;
+  __shared__ float s_w0_inv_std3;
+  __shared__ float s_bias;
+
+  if (threadIdx.x == 0) {
+    float w0 = (float)weight[0];
+    float w1 = (float)weight[1];
+    float w2 = (float)weight[2];
+    s_bias = (float)bias[0];
+
+    s_w2_inv_std = w2 * rsqrtf(variance / hidden_size + epsilon);
+    s_w1_inv_std2 = w1 * rsqrtf(variance2 / hidden_size + epsilon);
+    s_w0_inv_std3 = w0 * rsqrtf(variance3 / hidden_size + epsilon);
+  }
+  __syncthreads();
+
+  for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
+    float x = (float)input[blockIdx.x * hidden_size + idx];
+    float x2 = x * x;
+    float x3 = x2 * x;
+
+    out[blockIdx.x * hidden_size + idx] =
+        (scalar_t)(x * s_w2_inv_std + x2 * s_w1_inv_std2 + x3 * s_w0_inv_std3 +
+                   s_bias);
+  }
+}
+
 }  // namespace vllm
 
 void rms_norm(torch::Tensor& out,     // [..., hidden_size]
@@ -159,26 +364,18 @@ void rms_norm(torch::Tensor& out,     // [..., hidden_size]
   TORCH_CHECK(weight.is_contiguous());
 
   int hidden_size = input.size(-1);
-
-  // We cannot just use `input.stride(-2)` if the tensor is not row-major.
-  // Instead, we use a 2d view to get the second-innermost stride.
-  // That way the dimensions (except the last one) can be arbitrarily permuted.
-  torch::Tensor input_view = input.view({-1, hidden_size});
-
-  int num_tokens = input_view.numel() / hidden_size;
-  int64_t input_stride = input_view.stride(-2);
+  int num_tokens = input.numel() / hidden_size;
+  int64_t input_stride = input.stride(-2);
 
   dim3 grid(num_tokens);
   dim3 block(std::min(hidden_size, 1024));
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(input_view));
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  VLLM_DISPATCH_FLOATING_TYPES(
-      input_view.scalar_type(), "rms_norm_kernel", [&] {
-        vllm::rms_norm_kernel<scalar_t><<<grid, block, 0, stream>>>(
-            out.data_ptr<scalar_t>(), input_view.data_ptr<scalar_t>(),
-            input_stride, weight.data_ptr<scalar_t>(), epsilon, num_tokens,
-            hidden_size);
-      });
+  VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "rms_norm_kernel", [&] {
+    vllm::rms_norm_kernel<scalar_t><<<grid, block, 0, stream>>>(
+        out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), input_stride,
+        weight.data_ptr<scalar_t>(), epsilon, num_tokens, hidden_size);
+  });
 }
 
 #define LAUNCH_FUSED_ADD_RMS_NORM(width)                                    \
@@ -195,8 +392,6 @@ void fused_add_rms_norm(torch::Tensor& input,     // [..., hidden_size]
                         torch::Tensor& residual,  // [..., hidden_size]
                         torch::Tensor& weight,    // [hidden_size]
                         double epsilon) {
-  TORCH_CHECK(weight.scalar_type() == input.scalar_type());
-  TORCH_CHECK(input.scalar_type() == residual.scalar_type());
   TORCH_CHECK(residual.is_contiguous());
   TORCH_CHECK(weight.is_contiguous());
   int hidden_size = input.size(-1);
@@ -231,7 +426,7 @@ void fused_add_rms_norm(torch::Tensor& input,     // [..., hidden_size]
                           wt_ptr % req_alignment_bytes == 0;
   bool offsets_are_multiple_of_vector_width =
       hidden_size % vector_width == 0 && input_stride % vector_width == 0;
-  bool batch_invariant_launch = vllm::vllm_is_batch_invariant();
+  bool batch_invariant_launch = vllm::vllm_kernel_override_batch_invariant();
   if (ptrs_are_aligned && offsets_are_multiple_of_vector_width &&
       !batch_invariant_launch) {
     LAUNCH_FUSED_ADD_RMS_NORM(8);
@@ -239,3 +434,50 @@ void fused_add_rms_norm(torch::Tensor& input,     // [..., hidden_size]
     LAUNCH_FUSED_ADD_RMS_NORM(0);
   }
 }
+
+#define LAUNCH_FUSED_POLY_NORM(width)                                         \
+  VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "poly_norm_kernel", [&] { \
+    vllm::poly_norm_kernel<scalar_t, width><<<grid, block, 0, stream>>>(      \
+        out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(),                 \
+        weight.data_ptr<scalar_t>(), bias.data_ptr<scalar_t>(), epsilon,      \
+        hidden_size);                                                         \
+  });
+
+void poly_norm(torch::Tensor& out,     // [..., hidden_size]
+               torch::Tensor& input,   // [..., hidden_size]
+               torch::Tensor& weight,  // [3]
+               torch::Tensor& bias,    // [1]
+               double epsilon) {
+  TORCH_CHECK(out.is_contiguous());
+  TORCH_CHECK(input.is_contiguous());
+  TORCH_CHECK(out.data_ptr() != input.data_ptr());
+
+  int hidden_size = input.size(-1);
+  int num_tokens = input.numel() / hidden_size;
+
+  dim3 grid(num_tokens);
+  /* This kernel is memory-latency bound in many scenarios.
+     When num_tokens is large, a smaller block size allows
+     for increased block occupancy on CUs and better latency
+     hiding on global mem ops. */
+  const int max_block_size = (num_tokens < 256) ? 1024 : 256;
+  dim3 block(std::min(hidden_size, max_block_size));
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+  /*If the tensor types are FP16/BF16, try to use the optimized kernel
+    with packed + vectorized ops.
+    Max optimization is achieved with a width-8 vector of FP16/BF16s
+    since we can load at most 128 bits at once in a global memory op.
+    However, this requires each tensor's data to be aligned to 16
+    bytes.
+   */
+  auto inp_ptr = reinterpret_cast<std::uintptr_t>(input.data_ptr());
+  auto out_ptr = reinterpret_cast<std::uintptr_t>(out.data_ptr());
+  bool ptrs_are_aligned = inp_ptr % 16 == 0 && out_ptr % 16 == 0;
+  bool batch_invariant_launch = vllm::vllm_kernel_override_batch_invariant();
+  if (ptrs_are_aligned && hidden_size % 8 == 0 && !batch_invariant_launch) {
+    LAUNCH_FUSED_POLY_NORM(8);
+  } else {
+    LAUNCH_FUSED_POLY_NORM(0);
+  }
+}

csrc/layernorm_quant_kernels.cu

@@ -229,8 +229,6 @@ void fused_add_rms_norm_static_fp8_quant(
     double epsilon) {
   TORCH_CHECK(out.is_contiguous());
   TORCH_CHECK(residual.is_contiguous());
-  TORCH_CHECK(residual.scalar_type() == input.scalar_type());
-  TORCH_CHECK(weight.scalar_type() == input.scalar_type());
   int hidden_size = input.size(-1);
   int input_stride = input.stride(-2);
   int num_tokens = input.numel() / hidden_size;
@@ -256,7 +254,7 @@ void fused_add_rms_norm_static_fp8_quant(
   auto wt_ptr = reinterpret_cast<std::uintptr_t>(weight.data_ptr());
   bool ptrs_are_aligned =
       inp_ptr % 16 == 0 && res_ptr % 16 == 0 && wt_ptr % 16 == 0;
-  bool batch_invariant_launch = vllm::vllm_is_batch_invariant();
+  bool batch_invariant_launch = vllm::vllm_kernel_override_batch_invariant();
   if (ptrs_are_aligned && hidden_size % 8 == 0 && input_stride % 8 == 0 &&
       !batch_invariant_launch) {
     LAUNCH_FUSED_ADD_RMS_NORM(8);

csrc/moe/moe_align_sum_kernels.cu

@@ -8,77 +8,12 @@
 
 #include "../cuda_compat.h"
 #include "../dispatch_utils.h"
-#include "core/math.hpp"
 
 #define CEILDIV(x, y) (((x) + (y) - 1) / (y))
 
 namespace vllm {
 namespace moe {
 
-namespace batched_moe_align_block_size {
-
-// Note num_threads needs to be 1024 for BlockScan Reduction in the kernel.
-static constexpr int32_t num_threads = 1024;
-static constexpr int32_t num_blocks = 1;
-__global__ void batched_moe_align_block_size_kernel(
-    int32_t const num_batches, int32_t const max_tokens_per_batch,
-    int32_t const block_size, int32_t const* __restrict__ batch_num_tokens,
-    int32_t* __restrict__ sorted_ids, int32_t* __restrict__ block_ids,
-    int32_t* __restrict__ num_tokens_post_pad) {
-  // TODO(varun): This is a naive implementation. Could be optimized.
-
-  size_t const batch_id = threadIdx.x;
-  size_t const stride = blockDim.x * gridDim.x;
-  int32_t const num_blocks_per_batch =
-      CEILDIV(max_tokens_per_batch, block_size);
-  int32_t const sorted_ids_size =
-      num_blocks_per_batch * num_batches * block_size;
-  int32_t const block_ids_size = sorted_ids_size / block_size;
-  int32_t const SENTINEL =
-      num_batches * max_tokens_per_batch;  // To denote invalid entries.
-  // Intialize sorted_ids
-  for (size_t i = threadIdx.x; i < sorted_ids_size; i += stride) {
-    sorted_ids[i] = SENTINEL;
-  }
-  // Intialize expert_ids with -1
-  for (size_t i = threadIdx.x; i < block_ids_size; i += stride) {
-    block_ids[i] = -1;
-  }
-
-  int32_t b_num_tokens = 0;
-  if (batch_id < num_batches) {
-    b_num_tokens = batch_num_tokens[batch_id];
-  }
-  int32_t const ceil_b_num_tokens =
-      CEILDIV(b_num_tokens, block_size) * block_size;
-
-  // Compute prefix sum over token counts per expert
-  using BlockScan = cub::BlockScan<int32_t, 1024>;
-  __shared__ typename BlockScan::TempStorage temp_storage;
-  int cumsum_val;
-  BlockScan(temp_storage).ExclusiveSum(ceil_b_num_tokens, cumsum_val);
-  __syncthreads();
-
-  bool const is_last_batch = batch_id == (num_batches - 1);
-  if (is_last_batch) {
-    *num_tokens_post_pad = cumsum_val + ceil_b_num_tokens;
-  }
-
-  if (batch_id < num_batches) {
-    int32_t const batch_offset = batch_id * max_tokens_per_batch;
-    for (size_t i = 0; i < b_num_tokens; ++i) {
-      sorted_ids[cumsum_val + i] = batch_offset + i;
-    }
-
-    int32_t const block_start = cumsum_val / block_size;
-    int32_t const num_blocks = ceil_b_num_tokens / block_size;
-    for (size_t i = 0; i < num_blocks; ++i) {
-      block_ids[block_start + i] = batch_id;
-    }
-  }
-}
-}  // namespace batched_moe_align_block_size
-
 template <typename scalar_t>
 __global__ void moe_align_block_size_kernel(
     const scalar_t* __restrict__ topk_ids,
@@ -345,33 +280,6 @@ void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
       });
 }
 
-void batched_moe_align_block_size(int64_t max_tokens_per_batch,
-                                  int64_t block_size,
-                                  torch::Tensor const& batch_num_tokens,
-                                  torch::Tensor sorted_ids,
-                                  torch::Tensor batch_ids,
-                                  torch::Tensor num_tokens_post_pad) {
-  namespace batched_kernel = vllm::moe::batched_moe_align_block_size;
-
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  int32_t const B = batch_num_tokens.size(0);
-  int32_t const num_blocks_per_batch =
-      round_to_next_multiple_of(max_tokens_per_batch, block_size) / block_size;
-  int32_t const num_blocks = num_blocks_per_batch * B;
-  int64_t const sorted_ids_size = num_blocks * block_size;
-
-  TORCH_CHECK(sorted_ids.size(0) == sorted_ids_size);
-  TORCH_CHECK(batch_ids.size(0) == sorted_ids_size / block_size);
-  TORCH_CHECK(num_tokens_post_pad.size(0) == 1);
-  TORCH_CHECK(B <= batched_kernel::num_threads);
-
-  batched_kernel::batched_moe_align_block_size_kernel<<<
-      batched_kernel::num_blocks, batched_kernel::num_threads, 0, stream>>>(
-      B, max_tokens_per_batch, block_size, batch_num_tokens.data_ptr<int32_t>(),
-      sorted_ids.data_ptr<int32_t>(), batch_ids.data_ptr<int32_t>(),
-      num_tokens_post_pad.data_ptr<int32_t>());
-}
-
 void moe_sum(torch::Tensor& input,   // [num_tokens, topk, hidden_size]
              torch::Tensor& output)  // [num_tokens, hidden_size]
 {

csrc/moe/moe_lora_align_sum_kernels.cu

@@ -1,169 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <time.h>
-#include <torch/all.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-
-#include <ATen/ATen.h>
-#include <ATen/cuda/Atomic.cuh>
-
-#include "../cuda_compat.h"
-#include "../dispatch_utils.h"
-#include "core/math.hpp"
-
-namespace {
-
-__device__ __forceinline__ int32_t index(int32_t total_col, int32_t row,
-                                         int32_t col) {
-  return row * total_col + col;
-}
-
-}  // namespace
-
-// TODO: Refactor common parts with moe_align_sum_kernels
-template <typename scalar_t, typename token_cnts_t>
-__global__ void moe_lora_align_sum_kernel(
-    scalar_t* __restrict__ topk_ids, int32_t* token_lora_mapping,
-    int64_t block_size, int num_experts, int max_loras, size_t numel,
-    int max_num_tokens_padded, int max_num_m_blocks,
-    int32_t* __restrict__ sorted_token_ids, int32_t* __restrict__ expert_ids,
-    int topk_num, int32_t* total_tokens_post_pad) {
-  const size_t tokens_per_thread = div_ceil(numel, blockDim.x);
-  const size_t start_idx = threadIdx.x * tokens_per_thread;
-
-  int lora_id = blockIdx.x;
-  extern __shared__ int32_t shared_mem[];
-  int32_t* cumsum = shared_mem;
-  token_cnts_t* tokens_cnts = (token_cnts_t*)(shared_mem + num_experts + 1);
-
-  // Initialize sorted_token_ids with numel
-  for (size_t it = threadIdx.x; it < max_num_tokens_padded; it += blockDim.x) {
-    sorted_token_ids[lora_id * max_num_tokens_padded + it] = numel;
-  }
-
-  // Initialize expert_ids with -1
-  for (size_t it = threadIdx.x; it < max_num_m_blocks; it += blockDim.x) {
-    expert_ids[lora_id * max_num_m_blocks + it] = -1;
-  }
-
-  // Initialize total_tokens_post_pad with 0
-  if (threadIdx.x == 0) {
-    total_tokens_post_pad[lora_id] = 0;
-  }
-
-  for (int i = 0; i < num_experts; ++i) {
-    tokens_cnts[index(num_experts, threadIdx.x + 1, i)] = 0;
-  }
-
-  for (int i = start_idx; i < numel && i < start_idx + tokens_per_thread; ++i) {
-    int mask = token_lora_mapping[i / topk_num] == lora_id;
-    int idx = index(num_experts, threadIdx.x + 1, topk_ids[i]);
-    tokens_cnts[idx] += mask;
-  }
-
-  __syncthreads();
-
-  // For each expert we accumulate the token counts from the different threads.
-  if (threadIdx.x < num_experts) {
-    tokens_cnts[index(num_experts, 0, threadIdx.x)] = 0;
-    for (int i = 1; i <= blockDim.x; ++i) {
-      tokens_cnts[index(num_experts, i, threadIdx.x)] +=
-          tokens_cnts[index(num_experts, i - 1, threadIdx.x)];
-    }
-  }
-
-  __syncthreads();
-
-  // We accumulate the token counts of all experts in thread 0.
-  if (threadIdx.x == 0) {
-    cumsum[0] = 0;
-    for (int i = 1; i <= num_experts; ++i) {
-      cumsum[i] = cumsum[i - 1] +
-                  div_ceil(tokens_cnts[index(num_experts, blockDim.x, i - 1)],
-                           block_size) *
-                      block_size;
-    }
-    total_tokens_post_pad[lora_id] = static_cast<int32_t>(cumsum[num_experts]);
-  }
-
-  __syncthreads();
-
-  /**
-   * For each expert, each thread processes the tokens of the corresponding
-   * blocks and stores the corresponding expert_id for each block.
-   */
-  if (threadIdx.x < num_experts) {
-    for (int i = cumsum[threadIdx.x]; i < cumsum[threadIdx.x + 1];
-         i += block_size) {
-      expert_ids[index(max_num_m_blocks, lora_id, i / block_size)] =
-          threadIdx.x;
-    }
-  }
-
-  for (int i = start_idx; i < numel && i < start_idx + tokens_per_thread; ++i) {
-    int32_t expert_id = topk_ids[i];
-    /** The cumsum[expert_id] stores the starting index of the tokens that the
-     * expert with expert_id needs to process, and
-     * tokens_cnts[threadIdx.x][expert_id] stores the indices of the tokens
-     * processed by the expert with expert_id within the current thread's token
-     * shard.
-     */
-    int32_t rank_post_pad =
-        tokens_cnts[index(num_experts, threadIdx.x, expert_id)] +
-        cumsum[expert_id];
-
-    int mask = (int)token_lora_mapping[i / topk_num] == lora_id;
-    atomicAdd(
-        &sorted_token_ids[index(max_num_tokens_padded, lora_id, rank_post_pad)],
-        (i - numel) * mask);
-    tokens_cnts[index(num_experts, threadIdx.x, expert_id)] += mask;
-  }
-}
-
-void moe_lora_align_block_size(torch::Tensor topk_ids,
-                               torch::Tensor token_lora_mapping,
-                               int64_t num_experts, int64_t block_size,
-                               int64_t max_loras, int64_t max_num_tokens_padded,
-                               int64_t max_num_m_blocks,
-                               torch::Tensor sorted_token_ids,
-                               torch::Tensor expert_ids,
-                               torch::Tensor num_tokens_post_pad) {
-  const int topk_num = topk_ids.size(1);
-
-  TORCH_CHECK(block_size > 0, "block_size should be greater than 0. ");
-
-  int device_max_shared_mem;
-  auto dev = topk_ids.get_device();
-  cudaDeviceGetAttribute(&device_max_shared_mem,
-                         cudaDevAttrMaxSharedMemoryPerBlockOptin, dev);
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-
-  const int32_t num_thread = max((int32_t)num_experts, 128);  // WARP_SIZE,
-  TORCH_CHECK(num_thread <= 1024,
-              "num_thread must be less than 1024, "
-              "and fallback is not implemented yet.");
-  const int32_t shared_mem = (num_thread + 1) * num_experts * sizeof(int32_t) +
-                             (num_experts + 1) * sizeof(int32_t);
-
-  if (shared_mem > device_max_shared_mem) {
-    TORCH_CHECK(false,
-                "Shared memory usage exceeds device limit, and global memory "
-                "fallback is not implemented yet.");
-  }
-
-  VLLM_DISPATCH_INTEGRAL_TYPES(
-      topk_ids.scalar_type(), "moe_lora_align_sum_kernel", [&] {
-        dim3 blockDim(num_thread);
-        auto kernel = moe_lora_align_sum_kernel<scalar_t, int32_t>;
-        AT_CUDA_CHECK(VLLM_DevFuncAttribute_SET_MaxDynamicSharedMemorySize(
-            (void*)kernel, shared_mem));
-        kernel<<<max_loras, blockDim, shared_mem, stream>>>(
-            topk_ids.data_ptr<scalar_t>(),
-            token_lora_mapping.data_ptr<int32_t>(), block_size, num_experts,
-            max_loras, topk_ids.numel(), max_num_tokens_padded,
-            max_num_m_blocks, sorted_token_ids.data_ptr<int32_t>(),
-            expert_ids.data_ptr<int32_t>(), topk_num,
-            num_tokens_post_pad.data_ptr<int32_t>());
-      });
-}

csrc/moe/moe_ops.h

@@ -4,7 +4,7 @@
 
 void topk_softmax(torch::Tensor& topk_weights, torch::Tensor& topk_indices,
                   torch::Tensor& token_expert_indices,
-                  torch::Tensor& gating_output, bool renormalize);
+                  torch::Tensor& gating_output);
 
 void moe_sum(torch::Tensor& input, torch::Tensor& output);
 
@@ -12,22 +12,6 @@ void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
                           int64_t block_size, torch::Tensor sorted_token_ids,
                           torch::Tensor experts_ids,
                           torch::Tensor num_tokens_post_pad);
-
-void batched_moe_align_block_size(int64_t max_tokens_per_batch,
-                                  int64_t block_size,
-                                  torch::Tensor const& expert_num_tokens,
-                                  torch::Tensor sorted_ids,
-                                  torch::Tensor expert_ids,
-                                  torch::Tensor num_tokens_post_pad);
-
-void moe_lora_align_block_size(torch::Tensor topk_ids,
-                               torch::Tensor token_lora_mapping,
-                               int64_t num_experts, int64_t block_size,
-                               int64_t max_loras, int64_t max_num_tokens_padded,
-                               int64_t max_num_m_blocks,
-                               torch::Tensor sorted_token_ids,
-                               torch::Tensor expert_ids,
-                               torch::Tensor num_tokens_post_pad);
 #ifndef USE_ROCM
 torch::Tensor moe_wna16_gemm(torch::Tensor input, torch::Tensor output,
                              torch::Tensor b_qweight, torch::Tensor b_scales,

csrc/moe/topk_softmax_kernels.cu

@@ -16,23 +16,12 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include <type_traits>
 #include <torch/all.h>
 #include <ATen/cuda/CUDAContext.h>
 #include <c10/cuda/CUDAGuard.h>
 #include "../cuda_compat.h"
 #include "../cub_helpers.h"
 
-#ifndef USE_ROCM
-    #include <cuda_bf16.h>
-    #include <cuda_fp16.h>
-#else
-    #include <hip/hip_bf16.h>
-    #include <hip/hip_fp16.h>
-    typedef __hip_bfloat16 __nv_bfloat16;
-    typedef __hip_bfloat162 __nv_bfloat162;
-#endif
-
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
 #define MIN(a, b) ((a) < (b) ? (a) : (b))
 
@@ -47,27 +36,16 @@ template <
     /// Alignment requirement in bytes
     int Alignment = sizeof(T) * N
 >
-struct alignas(Alignment) AlignedArray {
-    T data[N];
+class alignas(Alignment) AlignedArray {
+    float data[N];
 };
 
-template <typename T>
-__device__ __forceinline__ float toFloat(T value) {
-    if constexpr (std::is_same_v<T, float>) {
-        return value;
-    } else if constexpr (std::is_same_v<T, __nv_bfloat16>) {
-        return __bfloat162float(value);
-    } else if constexpr (std::is_same_v<T, __half>) {
-        return __half2float(value);
-    }
-}
-
 // ====================== Softmax things ===============================
 // We have our own implementation of softmax here so we can support transposing the output
 // in the softmax kernel when we extend this module to support expert-choice routing.
-template <int TPB, typename InputType>
+template <int TPB>
 __launch_bounds__(TPB) __global__
-    void moeSoftmax(const InputType* input, const bool* finished, float* output, const int num_cols)
+    void moeSoftmax(const float* input, const bool* finished, float* output, const int num_cols)
 {
     using BlockReduce = cub::BlockReduce<float, TPB>;
     __shared__ typename BlockReduce::TempStorage tmpStorage;
@@ -88,8 +66,7 @@ __launch_bounds__(TPB) __global__
     for (int ii = threadIdx.x; ii < num_cols; ii += TPB)
     {
         const int idx = thread_row_offset + ii;
-        const float val = toFloat(input[idx]);
-        threadData = max(val, threadData);
+        threadData = max(static_cast<float>(input[idx]), threadData);
     }
 
     const float maxElem = BlockReduce(tmpStorage).Reduce(threadData, CubMaxOp());
@@ -104,8 +81,7 @@ __launch_bounds__(TPB) __global__
     for (int ii = threadIdx.x; ii < num_cols; ii += TPB)
     {
         const int idx = thread_row_offset + ii;
-        const float val = toFloat(input[idx]);
-        threadData += expf(val - float_max);
+        threadData += exp((static_cast<float>(input[idx]) - float_max));
     }
 
     const auto Z = BlockReduce(tmpStorage).Reduce(threadData, CubAddOp());
@@ -119,9 +95,8 @@ __launch_bounds__(TPB) __global__
     for (int ii = threadIdx.x; ii < num_cols; ii += TPB)
     {
         const int idx = thread_row_offset + ii;
-        const float val = toFloat(input[idx]);
-        const float softmax_val = expf(val - float_max) * normalizing_factor;
-        output[idx] = softmax_val;
+        const float val = exp((static_cast<float>(input[idx]) - float_max)) * normalizing_factor;
+        output[idx] = val;
     }
 }
 
@@ -135,8 +110,7 @@ __launch_bounds__(TPB) __global__ void moeTopK(
     const int num_experts,
     const int k,
     const int start_expert,
-    const int end_expert,
-    const bool renormalize)
+    const int end_expert)
 {
 
     using cub_kvp = cub::KeyValuePair<int, float>;
@@ -151,7 +125,6 @@ __launch_bounds__(TPB) __global__ void moeTopK(
 
     const bool row_is_active = finished ? !finished[block_row] : true;
     const int thread_read_offset = blockIdx.x * num_experts;
-    float selected_sum = 0.f;
     for (int k_idx = 0; k_idx < k; ++k_idx)
     {
         thread_kvp.key = 0;
@@ -190,23 +163,9 @@ __launch_bounds__(TPB) __global__ void moeTopK(
             indices[idx] = should_process_row ? (expert - start_expert) : num_experts;
             assert(indices[idx] >= 0);
             source_rows[idx] = k_idx * num_rows + block_row;
-            if (renormalize) {
-                selected_sum += result_kvp.value;
-            }
         }
         __syncthreads();
     }
-
-    // Renormalize the k weights for this row to sum to 1, if requested.
-    if (renormalize) {
-        if (threadIdx.x == 0) {
-            const float denom = selected_sum > 0.f ? selected_sum : 1.f;
-            for (int k_idx = 0; k_idx < k; ++k_idx) {
-                const int idx = k * block_row + k_idx;
-                output[idx] = output[idx] / denom;
-            }
-        }
-    }
 }
 
 // ====================== TopK softmax things ===============================
@@ -225,30 +184,21 @@ __launch_bounds__(TPB) __global__ void moeTopK(
   2) This implementation assumes k is small, but will work for any k.
 */
 
-template <int VPT, int NUM_EXPERTS, int WARPS_PER_CTA, int BYTES_PER_LDG, int WARP_SIZE_PARAM, typename IndType, typename InputType = float>
+template <int VPT, int NUM_EXPERTS, int WARPS_PER_CTA, int BYTES_PER_LDG, int WARP_SIZE_PARAM, typename IndType>
 __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
-    void topkGatingSoftmax(const InputType* input, const bool* finished, float* output, const int num_rows, IndType* indices,
-        int* source_rows, const int k, const int start_expert, const int end_expert, const bool renormalize)
+    void topkGatingSoftmax(const float* input, const bool* finished, float* output, const int num_rows, IndType* indices,
+        int* source_rows, const int k, const int start_expert, const int end_expert)
 {
-    static_assert(std::is_same_v<InputType, float> || std::is_same_v<InputType, __nv_bfloat16> ||
-                      std::is_same_v<InputType, __half>,
-                  "InputType must be float, __nv_bfloat16, or __half");
-
     // We begin by enforcing compile time assertions and setting up compile time constants.
     static_assert(BYTES_PER_LDG == (BYTES_PER_LDG & -BYTES_PER_LDG), "BYTES_PER_LDG must be power of 2");
     static_assert(BYTES_PER_LDG <= 16, "BYTES_PER_LDG must be leq 16");
 
     // Number of bytes each thread pulls in per load
-    static constexpr int ELTS_PER_LDG = BYTES_PER_LDG / sizeof(InputType);
+    static constexpr int ELTS_PER_LDG = BYTES_PER_LDG / sizeof(float);
     static constexpr int ELTS_PER_ROW = NUM_EXPERTS;
     static constexpr int THREADS_PER_ROW = ELTS_PER_ROW / VPT;
     static constexpr int LDG_PER_THREAD = VPT / ELTS_PER_LDG;
 
-    if constexpr (std::is_same_v<InputType, __nv_bfloat16> || std::is_same_v<InputType, __half>) {
-        static_assert(ELTS_PER_LDG == 1 || ELTS_PER_LDG % 2 == 0,
-            "ELTS_PER_LDG must be 1 or even for 16-bit conversion");
-    }
-
     // Restrictions based on previous section.
     static_assert(VPT % ELTS_PER_LDG == 0, "The elements per thread must be a multiple of the elements per ldg");
     static_assert(WARP_SIZE_PARAM % THREADS_PER_ROW == 0, "The threads per row must cleanly divide the threads per warp");
@@ -286,71 +236,27 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
 
     // We finally start setting up the read pointers for each thread. First, each thread jumps to the start of the
     // row it will read.
-    const InputType* thread_row_ptr = input + thread_row * ELTS_PER_ROW;
+    const float* thread_row_ptr = input + thread_row * ELTS_PER_ROW;
 
     // Now, we compute the group each thread belong to in order to determine the first column to start loads.
     const int thread_group_idx = threadIdx.x % THREADS_PER_ROW;
     const int first_elt_read_by_thread = thread_group_idx * ELTS_PER_LDG;
-    const InputType* thread_read_ptr = thread_row_ptr + first_elt_read_by_thread;
+    const float* thread_read_ptr = thread_row_ptr + first_elt_read_by_thread;
+
+    // Determine the pointer type to use to read in the data depending on the BYTES_PER_LDG template param. In theory,
+    // this can support all powers of 2 up to 16.
+    // NOTE(woosuk): The original implementation uses CUTLASS aligned array here.
+    // We defined our own aligned array and use it here to avoid the dependency on CUTLASS.
+    using AccessType = AlignedArray<float, ELTS_PER_LDG>;
 
     // Finally, we pull in the data from global mem
     float row_chunk[VPT];
-
-    // NOTE(zhuhaoran): dispatch different input types loading, BF16/FP16 convert to float
-    if constexpr (std::is_same_v<InputType, float>) {
-        using VecType = AlignedArray<float, ELTS_PER_LDG>;
-        VecType* row_chunk_vec_ptr = reinterpret_cast<VecType*>(&row_chunk);
-        const VecType* vec_thread_read_ptr = reinterpret_cast<const VecType*>(thread_read_ptr);
+    AccessType* row_chunk_vec_ptr = reinterpret_cast<AccessType*>(&row_chunk);
+    const AccessType* vec_thread_read_ptr = reinterpret_cast<const AccessType*>(thread_read_ptr);
 #pragma unroll
-        for (int ii = 0; ii < LDG_PER_THREAD; ++ii) {
-            row_chunk_vec_ptr[ii] = vec_thread_read_ptr[ii * THREADS_PER_ROW];
-        }
-    } else if constexpr (std::is_same_v<InputType, __nv_bfloat16>) {
-        if constexpr (ELTS_PER_LDG >= 2) {
-            using VecType = AlignedArray<__nv_bfloat16, ELTS_PER_LDG>;
-            float2* row_chunk_f2 = reinterpret_cast<float2*>(row_chunk);
-            const VecType* vec_thread_read_ptr = reinterpret_cast<const VecType*>(thread_read_ptr);
-#pragma unroll
-            for (int ii = 0; ii < LDG_PER_THREAD; ++ii) {
-                VecType vec = vec_thread_read_ptr[ii * THREADS_PER_ROW];
-                int base_idx_f2 = ii * ELTS_PER_LDG / 2;
-#pragma unroll
-                for (int jj = 0; jj < ELTS_PER_LDG / 2; ++jj) {
-                    row_chunk_f2[base_idx_f2 + jj] = __bfloat1622float2(
-                        *reinterpret_cast<const __nv_bfloat162*>(vec.data + jj * 2)
-                    );
-                }
-            }
-        } else { // ELTS_PER_LDG == 1
-#pragma unroll
-            for (int ii = 0; ii < LDG_PER_THREAD; ++ii) {
-                const __nv_bfloat16* scalar_ptr = thread_read_ptr + ii * THREADS_PER_ROW;
-                row_chunk[ii] = __bfloat162float(*scalar_ptr);
-            }
-        }
-    } else if constexpr (std::is_same_v<InputType, __half>) {
-        if constexpr (ELTS_PER_LDG >= 2) {
-            using VecType = AlignedArray<__half, ELTS_PER_LDG>;
-            float2* row_chunk_f2 = reinterpret_cast<float2*>(row_chunk);
-            const VecType* vec_thread_read_ptr = reinterpret_cast<const VecType*>(thread_read_ptr);
-#pragma unroll
-            for (int ii = 0; ii < LDG_PER_THREAD; ++ii) {
-                VecType vec = vec_thread_read_ptr[ii * THREADS_PER_ROW];
-                int base_idx_f2 = ii * ELTS_PER_LDG / 2;
-#pragma unroll
-                for (int jj = 0; jj < ELTS_PER_LDG / 2; ++jj) {
-                    row_chunk_f2[base_idx_f2 + jj] = __half22float2(
-                        *reinterpret_cast<const __half2*>(vec.data + jj * 2)
-                    );
-                }
-            }
-        } else { // ELTS_PER_LDG == 1
-#pragma unroll
-            for (int ii = 0; ii < LDG_PER_THREAD; ++ii) {
-                const __half* scalar_ptr = thread_read_ptr + ii * THREADS_PER_ROW;
-                row_chunk[ii] = __half2float(*scalar_ptr);
-            }
-        }
+    for (int ii = 0; ii < LDG_PER_THREAD; ++ii)
+    {
+        row_chunk_vec_ptr[ii] = vec_thread_read_ptr[ii * THREADS_PER_ROW];
     }
 
     // First, we perform a max reduce within the thread. We can do the max in fp16 safely (I think) and just
@@ -404,7 +310,6 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
     int start_col = first_elt_read_by_thread;
     static constexpr int COLS_PER_GROUP_LDG = ELTS_PER_LDG * THREADS_PER_ROW;
 
-    float selected_sum = 0.f;
     for (int k_idx = 0; k_idx < k; ++k_idx)
     {
         // First, each thread does the local argmax
@@ -458,9 +363,6 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
             output[idx] = max_val;
             indices[idx] = should_process_row ? (expert - start_expert) : NUM_EXPERTS;
             source_rows[idx] = k_idx * num_rows + thread_row;
-            if (renormalize) {
-                selected_sum += max_val;
-            }
         }
 
         // Finally, we clear the value in the thread with the current max if there is another iteration to run.
@@ -478,28 +380,15 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
             }
         }
     }
-
-    // Renormalize the k weights for this row to sum to 1, if requested.
-    if (renormalize) {
-        if (thread_group_idx == 0)
-        {
-            const float denom = selected_sum > 0.f ? selected_sum : 1.f;
-            for (int k_idx = 0; k_idx < k; ++k_idx)
-            {
-                const int idx = k * thread_row + k_idx;
-                output[idx] = output[idx] / denom;
-            }
-        }
-    }
 }
 
 namespace detail
 {
 // Constructs some constants needed to partition the work across threads at compile time.
-template <int EXPERTS, int BYTES_PER_LDG, int WARP_SIZE_PARAM, typename InputType>
+template <int EXPERTS, int BYTES_PER_LDG, int WARP_SIZE_PARAM>
 struct TopkConstants
 {
-    static constexpr int ELTS_PER_LDG = BYTES_PER_LDG / sizeof(InputType);
+    static constexpr int ELTS_PER_LDG = BYTES_PER_LDG / sizeof(float);
     static_assert(EXPERTS / (ELTS_PER_LDG * WARP_SIZE_PARAM) == 0 || EXPERTS % (ELTS_PER_LDG * WARP_SIZE_PARAM) == 0, "");
     static constexpr int VECs_PER_THREAD = MAX(1, EXPERTS / (ELTS_PER_LDG * WARP_SIZE_PARAM));
     static constexpr int VPT = VECs_PER_THREAD * ELTS_PER_LDG;
@@ -508,21 +397,20 @@ struct TopkConstants
 };
 } // namespace detail
 
-template <int EXPERTS, int WARPS_PER_TB, int WARP_SIZE_PARAM, int MAX_BYTES_PER_LDG, typename IndType, typename InputType>
-void topkGatingSoftmaxLauncherHelper(const InputType* input, const bool* finished, float* output, IndType* indices,
-    int* source_row, const int num_rows, const int k, const int start_expert, const int end_expert, const bool renormalize,
-    cudaStream_t stream)
+template <int EXPERTS, int WARPS_PER_TB, int WARP_SIZE_PARAM, int MAX_BYTES_PER_LDG, typename IndType>
+void topkGatingSoftmaxLauncherHelper(const float* input, const bool* finished, float* output, IndType* indices,
+    int* source_row, const int num_rows, const int k, const int start_expert, const int end_expert, cudaStream_t stream)
 {
-    static constexpr int BYTES_PER_LDG = MIN(MAX_BYTES_PER_LDG, sizeof(InputType) * EXPERTS);
-    using Constants = detail::TopkConstants<EXPERTS, BYTES_PER_LDG, WARP_SIZE_PARAM, InputType>;
+    static constexpr int BYTES_PER_LDG = MIN(MAX_BYTES_PER_LDG, sizeof(float) * EXPERTS);
+    using Constants = detail::TopkConstants<EXPERTS, BYTES_PER_LDG, WARP_SIZE_PARAM>;
     static constexpr int VPT = Constants::VPT;
     static constexpr int ROWS_PER_WARP = Constants::ROWS_PER_WARP;
     const int num_warps = (num_rows + ROWS_PER_WARP - 1) / ROWS_PER_WARP;
     const int num_blocks = (num_warps + WARPS_PER_TB - 1) / WARPS_PER_TB;
 
     dim3 block_dim(WARP_SIZE_PARAM, WARPS_PER_TB);
-    topkGatingSoftmax<VPT, EXPERTS, WARPS_PER_TB, BYTES_PER_LDG, WARP_SIZE_PARAM, IndType, InputType><<<num_blocks, block_dim, 0, stream>>>(
-        input, finished, output, num_rows, indices, source_row, k, start_expert, end_expert, renormalize);
+    topkGatingSoftmax<VPT, EXPERTS, WARPS_PER_TB, BYTES_PER_LDG, WARP_SIZE_PARAM><<<num_blocks, block_dim, 0, stream>>>(
+        input, finished, output, num_rows, indices, source_row, k, start_expert, end_expert);
 }
 
 #ifndef USE_ROCM
@@ -530,26 +418,26 @@ void topkGatingSoftmaxLauncherHelper(const InputType* input, const bool* finishe
     static_assert(WARP_SIZE == 32,                                                    \
                   "Unsupported warp size. Only 32 is supported for CUDA");            \
     topkGatingSoftmaxLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, WARP_SIZE, MAX_BYTES>( \
-        gating_output, nullptr, topk_weights, topk_indices, token_expert_indices,     \
-        num_tokens, topk, 0, num_experts, renormalize, stream);
+        gating_output, nullptr, topk_weights, topk_indices,                           \
+        token_expert_indices, num_tokens, topk, 0, num_experts, stream);
 #else
 #define LAUNCH_SOFTMAX(NUM_EXPERTS, WARPS_PER_TB, MAX_BYTES)                             \
     if (WARP_SIZE == 64) {                                                               \
         topkGatingSoftmaxLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 64, MAX_BYTES>(       \
-            gating_output, nullptr, topk_weights, topk_indices, token_expert_indices,    \
-            num_tokens, topk, 0, num_experts, renormalize, stream);                      \
+            gating_output, nullptr, topk_weights, topk_indices,                          \
+            token_expert_indices, num_tokens, topk, 0, num_experts, stream);             \
     } else if (WARP_SIZE == 32) {                                                        \
         topkGatingSoftmaxLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 32, MAX_BYTES>(       \
-            gating_output, nullptr, topk_weights, topk_indices, token_expert_indices,    \
-            num_tokens, topk, 0, num_experts, renormalize, stream);                      \
+            gating_output, nullptr, topk_weights, topk_indices,                          \
+            token_expert_indices, num_tokens, topk, 0, num_experts, stream);             \
     } else {                                                                             \
         assert(false && "Unsupported warp size. Only 32 and 64 are supported for ROCm"); \
     }
 #endif
 
-template <typename IndType, typename InputType>
+template <typename IndType>
 void topkGatingSoftmaxKernelLauncher(
-    const InputType* gating_output,
+    const float* gating_output,
     float* topk_weights,
     IndType* topk_indices,
     int* token_expert_indices,
@@ -557,15 +445,11 @@ void topkGatingSoftmaxKernelLauncher(
     const int num_tokens,
     const int num_experts,
     const int topk,
-    const bool renormalize,
     cudaStream_t stream) {
     static constexpr int WARPS_PER_TB = 4;
     static constexpr int BYTES_PER_LDG_POWER_OF_2 = 16;
 #ifndef USE_ROCM
-    // for bfloat16 dtype, we need 4 bytes loading to make sure num_experts
-    // elements can be loaded by a warp
-    static constexpr int BYTES_PER_LDG_MULTIPLE_64 =
-    (std::is_same_v<InputType, __nv_bfloat16> || std::is_same_v<InputType, __half>) ? 4 : 8;
+    static constexpr int BYTES_PER_LDG_MULTIPLE_64 = 8;
 #endif
     switch (num_experts) {
         case 1:
@@ -622,11 +506,11 @@ void topkGatingSoftmaxKernelLauncher(
             TORCH_CHECK(softmax_workspace != nullptr,
                 "softmax_workspace must be provided for num_experts that are not a power of 2 or multiple of 64.");
             static constexpr int TPB = 256;
-            moeSoftmax<TPB, InputType><<<num_tokens, TPB, 0, stream>>>(
+            moeSoftmax<TPB><<<num_tokens, TPB, 0, stream>>>(
                 gating_output, nullptr, softmax_workspace, num_experts);
             moeTopK<TPB><<<num_tokens, TPB, 0, stream>>>(
                 softmax_workspace, nullptr, topk_weights, topk_indices, token_expert_indices,
-                num_experts, topk, 0, num_experts, renormalize);
+                num_experts, topk, 0, num_experts);
         }
     }
 }
@@ -634,50 +518,11 @@ void topkGatingSoftmaxKernelLauncher(
 } // namespace moe
 } // namespace vllm
 
-
-template<typename ComputeType>
-void dispatch_topk_softmax_launch(
-    torch::Tensor& gating_output,
-    torch::Tensor& topk_weights,
-    torch::Tensor& topk_indices,
-    torch::Tensor& token_expert_indices,
-    torch::Tensor& softmax_workspace,
-    int num_tokens, int num_experts, int topk, bool renormalize, cudaStream_t stream)
-{
-    if (topk_indices.scalar_type() == at::ScalarType::Int) {
-        vllm::moe::topkGatingSoftmaxKernelLauncher<int, ComputeType>(
-            reinterpret_cast<const ComputeType*>(gating_output.data_ptr()),
-            topk_weights.data_ptr<float>(),
-            topk_indices.data_ptr<int>(),
-            token_expert_indices.data_ptr<int>(),
-            softmax_workspace.data_ptr<float>(),
-            num_tokens, num_experts, topk, renormalize, stream);
-    } else if (topk_indices.scalar_type() == at::ScalarType::UInt32) {
-        vllm::moe::topkGatingSoftmaxKernelLauncher<uint32_t, ComputeType>(
-            reinterpret_cast<const ComputeType*>(gating_output.data_ptr()),
-            topk_weights.data_ptr<float>(),
-            topk_indices.data_ptr<uint32_t>(),
-            token_expert_indices.data_ptr<int>(),
-            softmax_workspace.data_ptr<float>(),
-            num_tokens, num_experts, topk, renormalize, stream);
-    } else {
-        TORCH_CHECK(topk_indices.scalar_type() == at::ScalarType::Long);
-        vllm::moe::topkGatingSoftmaxKernelLauncher<int64_t, ComputeType>(
-            reinterpret_cast<const ComputeType*>(gating_output.data_ptr()),
-            topk_weights.data_ptr<float>(),
-            topk_indices.data_ptr<int64_t>(),
-            token_expert_indices.data_ptr<int>(),
-            softmax_workspace.data_ptr<float>(),
-            num_tokens, num_experts, topk, renormalize, stream);
-    }
-}
-
 void topk_softmax(
     torch::Tensor& topk_weights,                // [num_tokens, topk]
     torch::Tensor& topk_indices,                // [num_tokens, topk]
     torch::Tensor& token_expert_indices,        // [num_tokens, topk]
-    torch::Tensor& gating_output,               // [num_tokens, num_experts]
-    bool renormalize)
+    torch::Tensor& gating_output)               // [num_tokens, num_experts]
 {
     const int num_experts = gating_output.size(-1);
     const auto num_tokens = gating_output.numel() / num_experts;
@@ -689,19 +534,45 @@ void topk_softmax(
 
     const at::cuda::OptionalCUDAGuard device_guard(device_of(gating_output));
     const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-    const auto workspace_options = gating_output.options().dtype(at::ScalarType::Float);
-    torch::Tensor softmax_workspace = torch::empty({workspace_size}, workspace_options);
+    torch::Tensor softmax_workspace = torch::empty({workspace_size}, gating_output.options());
 
-    if (gating_output.scalar_type() == at::ScalarType::Float) {
-        dispatch_topk_softmax_launch<float>(gating_output, topk_weights, topk_indices, 
-            token_expert_indices, softmax_workspace, num_tokens, num_experts, topk, renormalize, stream);
-    } else if (gating_output.scalar_type() == at::ScalarType::Half) {
-        dispatch_topk_softmax_launch<__half>(gating_output, topk_weights, topk_indices, 
-            token_expert_indices, softmax_workspace, num_tokens, num_experts, topk, renormalize, stream);
-    } else if (gating_output.scalar_type() == at::ScalarType::BFloat16) {
-        dispatch_topk_softmax_launch<__nv_bfloat16>(gating_output, topk_weights, topk_indices, 
-            token_expert_indices, softmax_workspace, num_tokens, num_experts, topk, renormalize, stream);
-    } else {
-        TORCH_CHECK(false, "Unsupported gating_output data type: ", gating_output.scalar_type());
+    if(topk_indices.scalar_type() == at::ScalarType::Int)
+    {
+        vllm::moe::topkGatingSoftmaxKernelLauncher(
+            gating_output.data_ptr<float>(),
+            topk_weights.data_ptr<float>(),
+            topk_indices.data_ptr<int>(),
+            token_expert_indices.data_ptr<int>(),
+            softmax_workspace.data_ptr<float>(),
+            num_tokens,
+            num_experts,
+            topk,
+            stream);
+    }
+    else if (topk_indices.scalar_type() == at::ScalarType::UInt32)
+    {
+        vllm::moe::topkGatingSoftmaxKernelLauncher(
+            gating_output.data_ptr<float>(),
+            topk_weights.data_ptr<float>(),
+            topk_indices.data_ptr<uint32_t>(),
+            token_expert_indices.data_ptr<int>(),
+            softmax_workspace.data_ptr<float>(),
+            num_tokens,
+            num_experts,
+            topk,
+            stream);
+    }
+    else {
+        TORCH_CHECK(topk_indices.scalar_type() == at::ScalarType::Long);
+        vllm::moe::topkGatingSoftmaxKernelLauncher(
+            gating_output.data_ptr<float>(),
+            topk_weights.data_ptr<float>(),
+            topk_indices.data_ptr<int64_t>(),
+            token_expert_indices.data_ptr<int>(),
+            softmax_workspace.data_ptr<float>(),
+            num_tokens,
+            num_experts,
+            topk,
+            stream);
     }
 }

csrc/moe/torch_bindings.cpp

@@ -5,7 +5,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
   // Apply topk softmax to the gating outputs.
   m.def(
       "topk_softmax(Tensor! topk_weights, Tensor! topk_indices, Tensor! "
-      "token_expert_indices, Tensor gating_output, bool renormalize) -> ()");
+      "token_expert_indices, Tensor gating_output) -> ()");
   m.impl("topk_softmax", torch::kCUDA, &topk_softmax);
 
   // Calculate the result of moe by summing up the partial results
@@ -22,31 +22,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
       "                     Tensor! num_tokens_post_pad) -> ()");
   m.impl("moe_align_block_size", torch::kCUDA, &moe_align_block_size);
 
-  // Aligning the number of tokens to be processed by each expert such
-  // that it is divisible by the block size, but for the batched case.
-  m.def(
-      "batched_moe_align_block_size(int max_tokens_per_batch,"
-      "                     int block_size, Tensor expert_num_tokens,"
-      "                     Tensor! sorted_token_ids,"
-      "                     Tensor! experts_ids,"
-      "                     Tensor! num_tokens_post_pad) -> ()");
-  m.impl("batched_moe_align_block_size", torch::kCUDA,
-         &batched_moe_align_block_size);
-
-  // Aligning the number of tokens to be processed by each expert such
-  // that it is divisible by the block size.
-  m.def(
-      "moe_lora_align_block_size(Tensor topk_ids,"
-      "                     Tensor token_lora_mapping,"
-      "                     int num_experts,"
-      "                     int block_size, int max_loras, "
-      "                     int max_num_tokens_padded, "
-      "                     int max_num_m_blocks, "
-      "                     Tensor !sorted_token_ids,"
-      "                     Tensor !experts_ids,"
-      "                     Tensor !num_tokens_post_pad) -> () ");
-  m.impl("moe_lora_align_block_size", torch::kCUDA, &moe_lora_align_block_size);
-
 #ifndef USE_ROCM
   m.def(
       "moe_wna16_gemm(Tensor input, Tensor! output, Tensor b_qweight, "

csrc/ops.h

@@ -92,6 +92,9 @@ void rms_norm(torch::Tensor& out, torch::Tensor& input, torch::Tensor& weight,
 void fused_add_rms_norm(torch::Tensor& input, torch::Tensor& residual,
                         torch::Tensor& weight, double epsilon);
 
+void poly_norm(torch::Tensor& out, torch::Tensor& input, torch::Tensor& weight,
+               torch::Tensor& bias, double epsilon);
+
 void apply_repetition_penalties_(torch::Tensor& logits,
                                  const torch::Tensor& prompt_mask,
                                  const torch::Tensor& output_mask,
@@ -99,11 +102,8 @@ void apply_repetition_penalties_(torch::Tensor& logits,
 
 void top_k_per_row(const torch::Tensor& logits, const torch::Tensor& rowStarts,
                    const torch::Tensor& rowEnds, torch::Tensor& indices,
-                   int64_t numRows, int64_t stride0, int64_t stride1);
-
-void top_k_per_row_decode(const torch::Tensor& logits, int64_t next_n,
-                          const torch::Tensor& seq_lens, torch::Tensor& indices,
-                          int64_t numRows, int64_t stride0, int64_t stride1);
+                   torch::Tensor& values, int64_t numRows, int64_t stride0,
+                   int64_t stride1);
 
 void rms_norm_static_fp8_quant(torch::Tensor& out, torch::Tensor& input,
                                torch::Tensor& weight, torch::Tensor& scale,
@@ -307,7 +307,7 @@ void dynamic_scaled_int8_quant(torch::Tensor& out, torch::Tensor const& input,
 torch::Tensor gptq_gemm(torch::Tensor a, torch::Tensor b_q_weight,
                         torch::Tensor b_gptq_qzeros,
                         torch::Tensor b_gptq_scales, torch::Tensor b_g_idx,
-                        bool use_exllama, bool use_v2_format, int64_t bit);
+                        bool use_exllama, int64_t bit);
 
 void gptq_shuffle(torch::Tensor q_weight, torch::Tensor q_perm, int64_t bit);
 

csrc/quantization/fused_kernels/fused_layernorm_dynamic_per_token_quant.cu

@@ -145,11 +145,7 @@ void rms_norm_dynamic_per_token_quant(
   if (scale_ub.has_value()) {
     TORCH_CHECK(out.dtype() == kFp8Type);
   }
-  TORCH_CHECK(weight.dtype() == input.dtype());
   TORCH_CHECK(scales.dtype() == torch::kFloat32);
-  if (residual) {
-    TORCH_CHECK(residual->scalar_type() == input.scalar_type());
-  }
 
   VLLM_DISPATCH_FLOATING_TYPES(
       input.scalar_type(), "rms_norm_dynamic_per_token_quant_dispatch", [&] {

csrc/quantization/gptq/q_gemm.cu

@@ -185,7 +185,7 @@ typedef void (*fp_gemm_half_q_half_gptq_kernel)(const half*, const uint32_t*,
                                                 const uint32_t*, const half*,
                                                 half*, const int, const int,
                                                 const int, const int,
-                                                const bool, const int*);
+                                                const int*);
 
 template <bool first_block, int m_count>
 __global__ void gemm_half_q_half_gptq_4bit_kernel(
@@ -193,15 +193,12 @@ __global__ void gemm_half_q_half_gptq_4bit_kernel(
     const uint32_t* __restrict__ b_gptq_qzeros,
     const half* __restrict__ b_gptq_scales, half* __restrict__ c,
     const int size_m, const int size_n, const int size_k, const int groups,
-    const bool use_v2_format, const int* __restrict__ b_q_perm) {
+    const int* __restrict__ b_q_perm) {
   MatrixView_half a_(a, size_m, size_k);
   MatrixView_half_rw c_(c, size_m, size_n);
   MatrixView_q4_row b_gptq_qzeros_(b_gptq_qzeros, groups, size_n);
   MatrixView_half b_gptq_scales_(b_gptq_scales, groups, size_n);
 
-  // GPTQv2 and GPTQv1 handles zero points differently
-  int zero_offset = use_v2_format ? 0 : 1;
-
   auto t = threadIdx.x;
 
   // Block
@@ -259,10 +256,10 @@ __global__ void gemm_half_q_half_gptq_4bit_kernel(
   half2 y1y16[4][2];
   b_gptq_qzeros_.item4(zeros, group, n);
   b_gptq_scales_.item4_f(scales, group, n);
-  dequant_4bit_8_prep_zero(zeros[0] + zero_offset, z1z16[0], y1y16[0]);
-  dequant_4bit_8_prep_zero(zeros[1] + zero_offset, z1z16[1], y1y16[1]);
-  dequant_4bit_8_prep_zero(zeros[2] + zero_offset, z1z16[2], y1y16[2]);
-  dequant_4bit_8_prep_zero(zeros[3] + zero_offset, z1z16[3], y1y16[3]);
+  dequant_4bit_8_prep_zero(zeros[0] + 1, z1z16[0], y1y16[0]);
+  dequant_4bit_8_prep_zero(zeros[1] + 1, z1z16[1], y1y16[1]);
+  dequant_4bit_8_prep_zero(zeros[2] + 1, z1z16[2], y1y16[2]);
+  dequant_4bit_8_prep_zero(zeros[3] + 1, z1z16[3], y1y16[3]);
 
   // Column result
   float block_c[m_count][4] = {};
@@ -275,10 +272,10 @@ __global__ void gemm_half_q_half_gptq_4bit_kernel(
       nextgroup += groupsize;
       b_gptq_qzeros_.item4(zeros, group, n);
       b_gptq_scales_.item4_f(scales, group, n);
-      dequant_4bit_8_prep_zero(zeros[0] + zero_offset, z1z16[0], y1y16[0]);
-      dequant_4bit_8_prep_zero(zeros[1] + zero_offset, z1z16[1], y1y16[1]);
-      dequant_4bit_8_prep_zero(zeros[2] + zero_offset, z1z16[2], y1y16[2]);
-      dequant_4bit_8_prep_zero(zeros[3] + zero_offset, z1z16[3], y1y16[3]);
+      dequant_4bit_8_prep_zero(zeros[0] + 1, z1z16[0], y1y16[0]);
+      dequant_4bit_8_prep_zero(zeros[1] + 1, z1z16[1], y1y16[1]);
+      dequant_4bit_8_prep_zero(zeros[2] + 1, z1z16[2], y1y16[2]);
+      dequant_4bit_8_prep_zero(zeros[3] + 1, z1z16[3], y1y16[3]);
     }
 
 #pragma unroll
@@ -332,15 +329,12 @@ __global__ void gemm_half_q_half_gptq_2bit_kernel(
     const uint32_t* __restrict__ b_gptq_qzeros,
     const half* __restrict__ b_gptq_scales, half* __restrict__ c,
     const int size_m, const int size_n, const int size_k, const int groups,
-    const bool use_v2_format, const int* __restrict__ b_q_perm) {
+    const int* __restrict__ b_q_perm) {
   MatrixView_half a_(a, size_m, size_k);
   MatrixView_half_rw c_(c, size_m, size_n);
   MatrixView_q2_row b_gptq_qzeros_(b_gptq_qzeros, groups, size_n);
   MatrixView_half b_gptq_scales_(b_gptq_scales, groups, size_n);
 
-  // GPTQv2 and GPTQv1 handles zero points differently
-  int zero_offset = use_v2_format ? 0 : 1;
-
   auto t = threadIdx.x;
 
   // Block
@@ -415,10 +409,10 @@ __global__ void gemm_half_q_half_gptq_2bit_kernel(
       int4 load_int4 = *b_ptr4;
 
       half2 dq[4][8];
-      dequant_2bit_16(load_int4.x, dq[0], size_n, zeros[0] + zero_offset);
-      dequant_2bit_16(load_int4.y, dq[1], size_n, zeros[1] + zero_offset);
-      dequant_2bit_16(load_int4.z, dq[2], size_n, zeros[2] + zero_offset);
-      dequant_2bit_16(load_int4.w, dq[3], size_n, zeros[3] + zero_offset);
+      dequant_2bit_16(load_int4.x, dq[0], size_n, zeros[0] + 1);
+      dequant_2bit_16(load_int4.y, dq[1], size_n, zeros[1] + 1);
+      dequant_2bit_16(load_int4.z, dq[2], size_n, zeros[2] + 1);
+      dequant_2bit_16(load_int4.w, dq[3], size_n, zeros[3] + 1);
 
 #pragma unroll
       for (int m = 0; m < m_count; m++) {
@@ -454,15 +448,12 @@ __global__ void gemm_half_q_half_gptq_3bit_kernel(
     const uint32_t* __restrict__ b_gptq_qzeros,
     const half* __restrict__ b_gptq_scales, half* __restrict__ c,
     const int size_m, const int size_n, const int size_k, const int groups,
-    const bool use_v2_format, const int* __restrict__ b_q_perm) {
+    const int* __restrict__ b_q_perm) {
   MatrixView_half a_(a, size_m, size_k);
   MatrixView_half_rw c_(c, size_m, size_n);
   MatrixView_q3_row b_gptq_qzeros_(b_gptq_qzeros, groups, size_n);
   MatrixView_half b_gptq_scales_(b_gptq_scales, groups, size_n);
 
-  // GPTQv2 and GPTQv1 handles zero points differently
-  int zero_offset = use_v2_format ? 0 : 1;
-
   auto t = threadIdx.x;
 
   // Block
@@ -543,13 +534,13 @@ __global__ void gemm_half_q_half_gptq_3bit_kernel(
 
       half2 dq[4][16];
       dequant_3bit_32(load_int4[0].x, load_int4[1].x, load_int4[2].x, dq[0],
-                      size_n, zeros[0] + zero_offset);
+                      size_n, zeros[0] + 1);
       dequant_3bit_32(load_int4[0].y, load_int4[1].y, load_int4[2].y, dq[1],
-                      size_n, zeros[1] + zero_offset);
+                      size_n, zeros[1] + 1);
       dequant_3bit_32(load_int4[0].z, load_int4[1].z, load_int4[2].z, dq[2],
-                      size_n, zeros[2] + zero_offset);
+                      size_n, zeros[2] + 1);
       dequant_3bit_32(load_int4[0].w, load_int4[1].w, load_int4[2].w, dq[3],
-                      size_n, zeros[3] + zero_offset);
+                      size_n, zeros[3] + 1);
 
 #pragma unroll
       for (int m = 0; m < m_count; m++) {
@@ -583,15 +574,12 @@ __global__ void gemm_half_q_half_gptq_8bit_kernel(
     const uint32_t* __restrict__ b_gptq_qzeros,
     const half* __restrict__ b_gptq_scales, half* __restrict__ c,
     const int size_m, const int size_n, const int size_k, const int groups,
-    const bool use_v2_format, const int* __restrict__ b_q_perm) {
+    const int* __restrict__ b_q_perm) {
   MatrixView_half a_(a, size_m, size_k);
   MatrixView_half_rw c_(c, size_m, size_n);
   MatrixView_q8_row b_gptq_qzeros_(b_gptq_qzeros, groups, size_n);
   MatrixView_half b_gptq_scales_(b_gptq_scales, groups, size_n);
 
-  // GPTQv2 and GPTQv1 handles zero points differently
-  int zero_offset = use_v2_format ? 0 : 1;
-
   auto t = threadIdx.x;
 
   // Block
@@ -670,13 +658,13 @@ __global__ void gemm_half_q_half_gptq_8bit_kernel(
 
       half2 dq[4][4];
       dequant_8bit_8(load_int4[0].x, load_int4[1].x, dq[0], size_n,
-                     zeros[0] + zero_offset);
+                     zeros[0] + 1);
       dequant_8bit_8(load_int4[0].y, load_int4[1].y, dq[1], size_n,
-                     zeros[1] + zero_offset);
+                     zeros[1] + 1);
       dequant_8bit_8(load_int4[0].z, load_int4[1].z, dq[2], size_n,
-                     zeros[2] + zero_offset);
+                     zeros[2] + 1);
       dequant_8bit_8(load_int4[0].w, load_int4[1].w, dq[3], size_n,
-                     zeros[3] + zero_offset);
+                     zeros[3] + 1);
 
       for (int m = 0; m < m_count; m++) {
         block_c[m][0] =
@@ -742,8 +730,7 @@ void gemm_half_q_half_cuda_part(const half* a, const uint32_t* b_q_weight,
                                 const uint32_t* b_gptq_qzeros,
                                 const half* b_gptq_scales, const int* b_q_perm,
                                 half* c, int size_m, int size_n, int size_k,
-                                int m_count, int groups, bool use_v2_format,
-                                int bit) {
+                                int m_count, int groups, int bit) {
   dim3 blockDim, gridDim;
   blockDim.x = BLOCK_KN_SIZE;
   blockDim.y = 1;
@@ -756,23 +743,20 @@ void gemm_half_q_half_cuda_part(const half* a, const uint32_t* b_q_weight,
       pick_gemm_half_q_half_gptq_kernel(true, m_count, bit);
 
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  kernel<<<gridDim, blockDim, 0, stream>>>(
-      a, b_q_weight, b_gptq_qzeros, b_gptq_scales, c, size_m, size_n, size_k,
-      groups, use_v2_format, b_q_perm);
+  kernel<<<gridDim, blockDim, 0, stream>>>(a, b_q_weight, b_gptq_qzeros,
+                                           b_gptq_scales, c, size_m, size_n,
+                                           size_k, groups, b_q_perm);
 }
 
 __global__ void reconstruct_exllama_8bit_kernel(
     const uint32_t* __restrict__ b_q_weight, const int* __restrict__ b_q_perm,
     const uint32_t* __restrict__ b_gptq_qzeros,
     const half* __restrict__ b_gptq_scales, const int size_k, const int size_n,
-    const int groups, const bool use_v2_format, half* __restrict__ b) {
+    const int groups, half* __restrict__ b) {
   MatrixView_half_rw b_(b, size_k, size_n);
   MatrixView_q8_row b_gptq_qzeros_(b_gptq_qzeros, groups, size_n);
   MatrixView_half b_gptq_scales_(b_gptq_scales, groups, size_n);
 
-  // GPTQv2 and GPTQv1 handles zero points differently
-  int zero_offset = use_v2_format ? 0 : 1;
-
   auto offset_k = BLOCK_KN_SIZE * blockIdx.y;
   auto offset_n = BLOCK_KN_SIZE * blockIdx.x * 4;
 
@@ -828,13 +812,13 @@ __global__ void reconstruct_exllama_8bit_kernel(
 
       half2 dq[4][4];
       dequant_8bit_8(load_int4[0].x, load_int4[1].x, dq[0], size_n,
-                     zeros[0] + zero_offset);
+                     zeros[0] + 1);
       dequant_8bit_8(load_int4[0].y, load_int4[1].y, dq[1], size_n,
-                     zeros[1] + zero_offset);
+                     zeros[1] + 1);
       dequant_8bit_8(load_int4[0].z, load_int4[1].z, dq[2], size_n,
-                     zeros[2] + zero_offset);
+                     zeros[2] + 1);
       dequant_8bit_8(load_int4[0].w, load_int4[1].w, dq[3], size_n,
-                     zeros[3] + zero_offset);
+                     zeros[3] + 1);
 
       // half* dqh = (half*)dq;
       if (b_q_perm) {
@@ -865,14 +849,11 @@ __global__ void reconstruct_exllama_4bit_kernel(
     const uint32_t* __restrict__ b_q_weight, const int* __restrict__ b_q_perm,
     const uint32_t* __restrict__ b_gptq_qzeros,
     const half* __restrict__ b_gptq_scales, const int size_k, const int size_n,
-    const int groups, const bool use_v2_format, half* __restrict__ b) {
+    const int groups, half* __restrict__ b) {
   MatrixView_half_rw b_(b, size_k, size_n);
   MatrixView_q4_row b_gptq_qzeros_(b_gptq_qzeros, groups, size_n);
   MatrixView_half b_gptq_scales_(b_gptq_scales, groups, size_n);
 
-  // GPTQv2 and GPTQv1 handles zero points differently
-  int zero_offset = use_v2_format ? 0 : 1;
-
   auto offset_k = BLOCK_KN_SIZE * blockIdx.y;
   auto offset_n = BLOCK_KN_SIZE * blockIdx.x * 4;
 
@@ -907,10 +888,10 @@ __global__ void reconstruct_exllama_4bit_kernel(
   half2 y1y16[4][2];
   b_gptq_qzeros_.item4(zeros, group, n);
   b_gptq_scales_.item4_h2(scales, group, n);
-  dequant_4bit_8_prep_zero(zeros[0] + zero_offset, z1z16[0], y1y16[0]);
-  dequant_4bit_8_prep_zero(zeros[1] + zero_offset, z1z16[1], y1y16[1]);
-  dequant_4bit_8_prep_zero(zeros[2] + zero_offset, z1z16[2], y1y16[2]);
-  dequant_4bit_8_prep_zero(zeros[3] + zero_offset, z1z16[3], y1y16[3]);
+  dequant_4bit_8_prep_zero(zeros[0] + 1, z1z16[0], y1y16[0]);
+  dequant_4bit_8_prep_zero(zeros[1] + 1, z1z16[1], y1y16[1]);
+  dequant_4bit_8_prep_zero(zeros[2] + 1, z1z16[2], y1y16[2]);
+  dequant_4bit_8_prep_zero(zeros[3] + 1, z1z16[3], y1y16[3]);
 
   __syncthreads();
 
@@ -923,10 +904,10 @@ __global__ void reconstruct_exllama_4bit_kernel(
       nextgroup += groupsize;
       b_gptq_qzeros_.item4(zeros, group, n);
       b_gptq_scales_.item4_h2(scales, group, n);
-      dequant_4bit_8_prep_zero(zeros[0] + zero_offset, z1z16[0], y1y16[0]);
-      dequant_4bit_8_prep_zero(zeros[1] + zero_offset, z1z16[1], y1y16[1]);
-      dequant_4bit_8_prep_zero(zeros[2] + zero_offset, z1z16[2], y1y16[2]);
-      dequant_4bit_8_prep_zero(zeros[3] + zero_offset, z1z16[3], y1y16[3]);
+      dequant_4bit_8_prep_zero(zeros[0] + 1, z1z16[0], y1y16[0]);
+      dequant_4bit_8_prep_zero(zeros[1] + 1, z1z16[1], y1y16[1]);
+      dequant_4bit_8_prep_zero(zeros[2] + 1, z1z16[2], y1y16[2]);
+      dequant_4bit_8_prep_zero(zeros[3] + 1, z1z16[3], y1y16[3]);
     }
 
     for (int p = 0; p < 4; p++) {
@@ -973,14 +954,11 @@ __global__ void reconstruct_exllama_3bit_kernel(
     const uint32_t* __restrict__ b_q_weight, const int* __restrict__ b_q_perm,
     const uint32_t* __restrict__ b_gptq_qzeros,
     const half* __restrict__ b_gptq_scales, const int size_k, const int size_n,
-    const int groups, const bool use_v2_format, half* __restrict__ b) {
+    const int groups, half* __restrict__ b) {
   MatrixView_half_rw b_(b, size_k, size_n);
   MatrixView_q3_row b_gptq_qzeros_(b_gptq_qzeros, groups, size_n);
   MatrixView_half b_gptq_scales_(b_gptq_scales, groups, size_n);
 
-  // GPTQv2 and GPTQv1 handles zero points differently
-  int zero_offset = use_v2_format ? 0 : 1;
-
   auto offset_k = BLOCK_KN_SIZE * blockIdx.y;
   auto offset_n = BLOCK_KN_SIZE * blockIdx.x * 4;
 
@@ -1038,13 +1016,13 @@ __global__ void reconstruct_exllama_3bit_kernel(
 
       half2 dq[4][16];
       dequant_3bit_32(load_int4[0].x, load_int4[1].x, load_int4[2].x, dq[0],
-                      size_n, zeros[0] + zero_offset);
+                      size_n, zeros[0] + 1);
       dequant_3bit_32(load_int4[0].y, load_int4[1].y, load_int4[2].y, dq[1],
-                      size_n, zeros[1] + zero_offset);
+                      size_n, zeros[1] + 1);
       dequant_3bit_32(load_int4[0].z, load_int4[1].z, load_int4[2].z, dq[2],
-                      size_n, zeros[2] + zero_offset);
+                      size_n, zeros[2] + 1);
       dequant_3bit_32(load_int4[0].w, load_int4[1].w, load_int4[2].w, dq[3],
-                      size_n, zeros[3] + zero_offset);
+                      size_n, zeros[3] + 1);
 
       if (b_q_perm) {
         for (int j = 0; j < 16; j++) {
@@ -1074,14 +1052,11 @@ __global__ void reconstruct_exllama_2bit_kernel(
     const uint32_t* __restrict__ b_q_weight, const int* __restrict__ b_q_perm,
     const uint32_t* __restrict__ b_gptq_qzeros,
     const half* __restrict__ b_gptq_scales, const int size_k, const int size_n,
-    const int groups, const bool use_v2_format, half* __restrict__ b) {
+    const int groups, half* __restrict__ b) {
   MatrixView_half_rw b_(b, size_k, size_n);
   MatrixView_q2_row b_gptq_qzeros_(b_gptq_qzeros, groups, size_n);
   MatrixView_half b_gptq_scales_(b_gptq_scales, groups, size_n);
 
-  // GPTQv2 and GPTQv1 handles zero points differently
-  int zero_offset = use_v2_format ? 0 : 1;
-
   auto offset_k = BLOCK_KN_SIZE * blockIdx.y;
   auto offset_n = BLOCK_KN_SIZE * blockIdx.x * 4;
 
@@ -1133,10 +1108,10 @@ __global__ void reconstruct_exllama_2bit_kernel(
       int4 load_int4 = *b_ptr4;
 
       half2 dq[4][8];
-      dequant_2bit_16(load_int4.x, dq[0], size_n, zeros[0] + zero_offset);
-      dequant_2bit_16(load_int4.y, dq[1], size_n, zeros[1] + zero_offset);
-      dequant_2bit_16(load_int4.z, dq[2], size_n, zeros[2] + zero_offset);
-      dequant_2bit_16(load_int4.w, dq[3], size_n, zeros[3] + zero_offset);
+      dequant_2bit_16(load_int4.x, dq[0], size_n, zeros[0] + 1);
+      dequant_2bit_16(load_int4.y, dq[1], size_n, zeros[1] + 1);
+      dequant_2bit_16(load_int4.z, dq[2], size_n, zeros[2] + 1);
+      dequant_2bit_16(load_int4.w, dq[3], size_n, zeros[3] + 1);
 
       b_ptr += size_n;
       // half* dqh = (half*)dq;
@@ -1168,7 +1143,7 @@ void reconstruct_exllama(const uint32_t* b_q_weight,
                          const uint32_t* b_gptq_qzeros,
                          const half* b_gptq_scales, const int* b_q_perm,
                          half* out, int height, int width, int groups,
-                         bool use_v2_format, int bit) {
+                         int bit) {
   dim3 blockDim, gridDim;
   blockDim.x = BLOCK_KN_SIZE;
   blockDim.y = 1;
@@ -1187,14 +1162,14 @@ void reconstruct_exllama(const uint32_t* b_q_weight,
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
   reconstruct_exllama_kernel<<<gridDim, blockDim, 0, stream>>>(
       b_q_weight, b_q_perm, b_gptq_qzeros, b_gptq_scales, height, width, groups,
-      use_v2_format, out);
+      out);
 }
 
 __global__ void gemm_half_q_half_alt_4bit_kernel(
     const half2* __restrict__ vec, const uint32_t* __restrict__ mat,
     half* __restrict__ mul, const half* __restrict__ scales,
     const uint32_t* __restrict__ zeros, const int* __restrict__ g_idx,
-    int batch, int height, int width, bool use_v2_format) {
+    int batch, int height, int width) {
   int zero_width = width / 8;
   int vec_height = height * 4;
   const int blockwidth2 = BLOCK_KN_SIZE / 2;
@@ -1204,9 +1179,6 @@ __global__ void gemm_half_q_half_alt_4bit_kernel(
   int h_end = min(BLOCK_KN_SIZE / 8, height - h) * 4;
   auto w = BLOCK_KN_SIZE * blockIdx.x + threadIdx.x;
 
-  // GPTQv2 and GPTQv1 handles zero points differently
-  int zero_offset = use_v2_format ? 0 : 1;
-
   __shared__ half2 blockvec[BLOCK_M_SIZE_MAX][blockwidth2];
   if (threadIdx.x < h_end) {
     for (int m = 0; m < b_end; ++m) {
@@ -1251,11 +1223,10 @@ __global__ void gemm_half_q_half_alt_4bit_kernel(
       half2 zero = __halves2half2(
           __hmul(scale_f,
                  __int2half_rn(-((zeros[g * zero_width + z_w] >> z_mod) & 0xF) -
-                               zero_offset)),
-          __hmul(
-              scale_f2,
-              __int2half_rn(-((zeros[g2 * zero_width + z_w] >> z_mod) & 0xF) -
-                            zero_offset)));
+                               1)),
+          __hmul(scale_f2,
+                 __int2half_rn(
+                     -((zeros[g2 * zero_width + z_w] >> z_mod) & 0xF) - 1)));
       scales_tmp[tmp_k] = scale;
       zeros_tmp[tmp_k] = zero;
     }
@@ -1297,7 +1268,7 @@ __global__ void gemm_half_q_half_alt_8bit_kernel(
     const half2* __restrict__ vec, const uint32_t* __restrict__ mat,
     half* __restrict__ mul, const half* __restrict__ scales,
     const uint32_t* __restrict__ zeros, const int* __restrict__ g_idx,
-    int batch, int height, int width, bool use_v2_format) {
+    int batch, int height, int width) {
   int zero_width = width / 4;
   int vec_height = height * 2;
   const int blockwidth2 = BLOCK_KN_SIZE / 2;
@@ -1307,9 +1278,6 @@ __global__ void gemm_half_q_half_alt_8bit_kernel(
   int h_end = min(BLOCK_KN_SIZE / 4, height - h) * 2;
   auto w = BLOCK_KN_SIZE * blockIdx.x + threadIdx.x;
 
-  // GPTQv2 and GPTQv1 handles zero points differently
-  int zero_offset = use_v2_format ? 0 : 1;
-
   __shared__ half2 blockvec[BLOCK_M_SIZE_MAX][blockwidth2];
   if (threadIdx.x < h_end) {
     for (int m = 0; m < b_end; ++m) {
@@ -1344,13 +1312,12 @@ __global__ void gemm_half_q_half_alt_8bit_kernel(
       half scale_f2 = scales[g2 * width + w];
       half2 scale = __halves2half2(scale_f, scale_f2);
       half2 zero = __halves2half2(
-          __hmul(scale_f, __int2half_rn(
-                              -((zeros[g * zero_width + z_w] >> z_mod) & 0xff) -
-                              zero_offset)),
-          __hmul(
-              scale_f2,
-              __int2half_rn(-((zeros[g2 * zero_width + z_w] >> z_mod) & 0xff) -
-                            zero_offset)));
+          __hmul(scale_f,
+                 __int2half_rn(
+                     -((zeros[g * zero_width + z_w] >> z_mod) & 0xff) - 1)),
+          __hmul(scale_f2,
+                 __int2half_rn(
+                     -((zeros[g2 * zero_width + z_w] >> z_mod) & 0xff) - 1)));
       scales_tmp[tmp_k] = scale;
       zeros_tmp[tmp_k] = zero;
     }
@@ -1388,7 +1355,7 @@ void gemm_half_q_half_alt(const half* a, const uint32_t* b_q_weight,
                           const uint32_t* b_gptq_qzeros,
                           const half* b_gptq_scales, const int* b_g_idx,
                           half* c, int size_m, int size_n, int size_k,
-                          bool use_v2_format, int bit) {
+                          int bit) {
   dim3 blockDim, gridDim;
   blockDim.x = BLOCK_KN_SIZE;
   blockDim.y = 1;
@@ -1405,15 +1372,17 @@ void gemm_half_q_half_alt(const half* a, const uint32_t* b_q_weight,
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
   kernel<<<gridDim, blockDim, 0, stream>>>(
       (const half2*)a, b_q_weight, c, b_gptq_scales, b_gptq_qzeros, b_g_idx,
-      size_m, size_k / 32 * bit, size_n, use_v2_format);
+      size_m, size_k / 32 * bit, size_n);
 }
 
 template <class T, int bit>
-__global__ void reconstruct_gptq_kernel(
-    const uint32_t* __restrict__ w, const half* __restrict__ w_scales,
-    const uint32_t* __restrict__ w_zeros, const int* __restrict__ g_idx,
-    const int height, const int width, const int group,
-    const bool use_v2_format, half* __restrict__ out) {
+__global__ void reconstruct_gptq_kernel(const uint32_t* __restrict__ w,
+                                        const half* __restrict__ w_scales,
+                                        const uint32_t* __restrict__ w_zeros,
+                                        const int* __restrict__ g_idx,
+                                        const int height, const int width,
+                                        const int group,
+                                        half* __restrict__ out) {
   // Start of block
 
   auto column = BLOCK_KN_SIZE * blockIdx.x + threadIdx.x;
@@ -1426,9 +1395,6 @@ __global__ void reconstruct_gptq_kernel(
   MatrixView_half w_scales_(w_scales, group, width);
   T w_zeros_(w_zeros, group, width);
 
-  // GPTQv2 and GPTQv1 handles zero points differently
-  int zero_offset = use_v2_format ? 0 : 1;
-
   uint32_t w_read = w[blockIdx.y * width + column];
   half* out_ptr = out_.item_ptr(row, column);
 
@@ -1436,7 +1402,7 @@ __global__ void reconstruct_gptq_kernel(
   for (int s = 0; s < 32; s += bit) {
     int group = g_idx[row + s / bit];
     half w_scale = w_scales_.item(group, column);
-    uint32_t w_zero = w_zeros_.item(group, column) + zero_offset;
+    uint32_t w_zero = w_zeros_.item(group, column) + 1;
     half w_item =
         __hmul(__int2half_rn((int)((w_read >> s) & ((1 << bit) - 1)) - w_zero),
                w_scale);
@@ -1449,7 +1415,7 @@ __global__ void reconstruct_gptq_3bit_kernel(
     const uint32_t* __restrict__ w, const half* __restrict__ w_scales,
     const uint32_t* __restrict__ w_zeros, const int* __restrict__ g_idx,
     const int height, const int width, const int group,
-    const bool use_v2_format, half* __restrict__ out) {
+    half* __restrict__ out) {
   // Start of block
   auto column = BLOCK_KN_SIZE * blockIdx.x + threadIdx.x;
   auto row = blockIdx.y * 32;
@@ -1461,9 +1427,6 @@ __global__ void reconstruct_gptq_3bit_kernel(
   MatrixView_half w_scales_(w_scales, group, width);
   MatrixView_q3_row w_zeros_(w_zeros, group, width);
 
-  // GPTQv2 and GPTQv1 handles zero points differently
-  int zero_offset = use_v2_format ? 0 : 1;
-
   uint32_t w1 = w[(blockIdx.y * 3) * width + column];
   uint32_t w2 = w[(blockIdx.y * 3 + 1) * width + column];
   uint32_t w3 = w[(blockIdx.y * 3 + 2) * width + column];
@@ -1473,7 +1436,7 @@ __global__ void reconstruct_gptq_3bit_kernel(
   for (int i = 0; i < 32; i += 1) {
     int group = g_idx[row + i];
     half w_scale = w_scales_.item(group, column);
-    uint32_t w_zero = w_zeros_.item(group, column) + zero_offset;
+    uint32_t w_zero = w_zeros_.item(group, column) + 1;
     int w_item;
     if (i == 10) {
       w_item = (w1 >> 30) | ((w2 << 2) & 0x4);
@@ -1493,8 +1456,7 @@ __global__ void reconstruct_gptq_3bit_kernel(
 
 void reconstruct_gptq(const uint32_t* b_q_weight, const uint32_t* b_gptq_qzeros,
                       const half* b_gptq_scales, const int* b_g_idx, half* out,
-                      int height, int width, int groups, bool use_v2_format,
-                      int bit) {
+                      int height, int width, int groups, int bit) {
   dim3 blockDim, gridDim;
   blockDim.x = BLOCK_KN_SIZE;
   blockDim.y = 1;
@@ -1514,7 +1476,7 @@ void reconstruct_gptq(const uint32_t* b_q_weight, const uint32_t* b_gptq_qzeros,
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
   kernel<<<gridDim, blockDim, 0, stream>>>(b_q_weight, b_gptq_scales,
                                            b_gptq_qzeros, b_g_idx, height,
-                                           width, groups, use_v2_format, out);
+                                           width, groups, out);
 }
 
 void gemm_half_q_half_cuda(cublasHandle_t cublas_handle, const half* a,
@@ -1522,8 +1484,7 @@ void gemm_half_q_half_cuda(cublasHandle_t cublas_handle, const half* a,
                            const uint32_t* b_gptq_qzeros,
                            const half* b_gptq_scales, const int* b_g_idx,
                            half* c, half* temp_dq, int size_m, int size_n,
-                           int size_k, int groups, bool use_exllama,
-                           bool use_v2_format, int bit) {
+                           int size_k, int groups, bool use_exllama, int bit) {
   bool use_reconstruct;
   if (use_exllama) {
     use_reconstruct = ((bit == 8 && size_m > MAX_Q_GEMM_ROWS_8BIT) ||
@@ -1537,10 +1498,10 @@ void gemm_half_q_half_cuda(cublasHandle_t cublas_handle, const half* a,
     // Reconstruct FP16 matrix, then cuBLAS
     if (use_exllama) {
       reconstruct_exllama(b_q_weight, b_gptq_qzeros, b_gptq_scales, b_g_idx,
-                          temp_dq, size_k, size_n, groups, use_v2_format, bit);
+                          temp_dq, size_k, size_n, groups, bit);
     } else {
       reconstruct_gptq(b_q_weight, b_gptq_qzeros, b_gptq_scales, b_g_idx,
-                       temp_dq, size_k, size_n, groups, use_v2_format, bit);
+                       temp_dq, size_k, size_n, groups, bit);
     }
 
     const half alpha = __float2half(1.0f);
@@ -1556,18 +1517,18 @@ void gemm_half_q_half_cuda(cublasHandle_t cublas_handle, const half* a,
     if (max_chunks) {
       gemm_half_q_half_cuda_part(a, b_q_weight, b_gptq_qzeros, b_gptq_scales,
                                  b_g_idx, c, last_chunk, size_n, size_k,
-                                 BLOCK_M_SIZE_MAX, groups, use_v2_format, bit);
+                                 BLOCK_M_SIZE_MAX, groups, bit);
     }
 
     if (last_chunk_size) {
-      gemm_half_q_half_cuda_part(
-          a + last_chunk * size_k, b_q_weight, b_gptq_qzeros, b_gptq_scales,
-          b_g_idx, c + last_chunk * size_n, last_chunk_size, size_n, size_k,
-          last_chunk_size, groups, use_v2_format, bit);
+      gemm_half_q_half_cuda_part(a + last_chunk * size_k, b_q_weight,
+                                 b_gptq_qzeros, b_gptq_scales, b_g_idx,
+                                 c + last_chunk * size_n, last_chunk_size,
+                                 size_n, size_k, last_chunk_size, groups, bit);
     }
   } else {
     gemm_half_q_half_alt(a, b_q_weight, b_gptq_qzeros, b_gptq_scales, b_g_idx,
-                         c, size_m, size_n, size_k, use_v2_format, bit);
+                         c, size_m, size_n, size_k, bit);
   }
 }
 
@@ -1854,7 +1815,7 @@ void shuffle_exllama_weight(uint32_t* q_weight, int* q_perm, int height,
 torch::Tensor gptq_gemm(torch::Tensor a, torch::Tensor b_q_weight,
                         torch::Tensor b_gptq_qzeros,
                         torch::Tensor b_gptq_scales, torch::Tensor b_g_idx,
-                        bool use_exllama, bool use_v2_format, int64_t bit) {
+                        bool use_exllama, int64_t bit) {
   const at::cuda::OptionalCUDAGuard device_guard(device_of(a));
   auto options = torch::TensorOptions().dtype(a.dtype()).device(a.device());
   at::Tensor c = torch::empty({a.size(0), b_q_weight.size(1)}, options);
@@ -1872,7 +1833,7 @@ torch::Tensor gptq_gemm(torch::Tensor a, torch::Tensor b_q_weight,
       c.size(1),              // n
       a.size(1),              // k
       b_gptq_qzeros.size(0),  // group number
-      use_exllama, use_v2_format, bit);
+      use_exllama, bit);
   return c;
 }
 

csrc/sampler.cu

@@ -54,10 +54,15 @@ static inline __device__ uint16_t extractBinIdx(float x) {
   return 511 - (tmp.u16 >> 7);
 }
 
-template <int kNumThreadsPerBlock = 512, int kNumBins = 512, int kTopK = 2048>
-__device__ void topKPerRowJob(const float* logits, const int rowStart,
-                              const int rowEnd, const int rowIdx,
-                              int* outIndices, int stride0, int stride1) {
+template <int kNumThreadsPerBlock = 512>
+static __global__ void topKPerRow(const float* logits, const int* rowStarts,
+                                  const int* rowEnds, int* outIndices,
+                                  float* outLogits, int stride0, int stride1) {
+  // The number of bins in the histogram.
+  static constexpr int kNumBins = 512;
+
+  // The top-k width.
+  static constexpr int kTopK = 2048;
   // The number of elements per thread for the final top-k sort.
   static constexpr int kNumTopKItemsPerThread = kTopK / kNumThreadsPerBlock;
   // The class to sort the elements during the final top-k sort.
@@ -98,11 +103,17 @@ __device__ void topKPerRowJob(const float* logits, const int rowStart,
   __shared__ int smemHistogram[kNumBins];
   // Shared memory to store the selected indices.
   __shared__ int smemIndices[kTopK];
+  // Shared memory to store the selected logits.
+  __shared__ float smemLogits[kTopK];
   // Shared memory to store the threshold bin.
   __shared__ int smemThresholdBinIdx[1];
   // Shared memory counter to register the candidates for the final phase.
   __shared__ int smemFinalDstIdx[1];
 
+  // The row computed by this block.
+  int rowIdx = blockIdx.x;
+  // The range of logits within the row.
+  int rowStart = rowStarts[rowIdx], rowEnd = rowEnds[rowIdx];
   // The length of the row.
   int rowLen = rowEnd - rowStart;
 
@@ -113,10 +124,13 @@ __device__ void topKPerRowJob(const float* logits, const int rowStart,
          rowIt += kNumThreadsPerBlock) {
       int idx = rowStart + rowIt;
       outIndices[rowIdx * kTopK + rowIt] = idx - rowStart;
+      outLogits[rowIdx * kTopK + rowIt] =
+          logits[rowIdx * stride0 + idx * stride1];
     }
     for (int rowIt = rowLen + threadIdx.x; rowIt < kTopK;
          rowIt += kNumThreadsPerBlock) {
       outIndices[rowIdx * kTopK + rowIt] = -1;
+      outLogits[rowIdx * kTopK + rowIt] = -FLT_MAX;
     }
     return;
   }
@@ -187,6 +201,7 @@ __device__ void topKPerRowJob(const float* logits, const int rowStart,
     uint16_t idx = extractBinIdx(logit);
     if (idx < thresholdBinIdx) {
       int dstIdx = atomicAdd(&smemHistogram[idx], 1);
+      smemLogits[dstIdx] = logit;
       smemIndices[dstIdx] = rowIt;
     } else if (idx == thresholdBinIdx) {
       int dstIdx = atomicAdd(&smemFinalDstIdx[0], 1);
@@ -235,6 +250,7 @@ __device__ void topKPerRowJob(const float* logits, const int rowStart,
     int srcIdx = ii * kNumThreadsPerBlock + threadIdx.x;
     int dstIdx = baseIdx + srcIdx;
     if (dstIdx < kTopK) {
+      smemLogits[dstIdx] = finalLogits[ii];
       smemIndices[dstIdx] = finalIndices[ii];
     }
   }
@@ -242,58 +258,31 @@ __device__ void topKPerRowJob(const float* logits, const int rowStart,
   // Make sure the data is in shared memory.
   __syncthreads();
 
+  // The topK logits.
+  float topKLogits[kNumTopKItemsPerThread];
+  // The topK indices.
+  int topKIndices[kNumTopKItemsPerThread];
+
+// Load from shared memory.
+#pragma unroll
+  for (int ii = 0; ii < kNumTopKItemsPerThread; ++ii) {
+    topKLogits[ii] = smemLogits[ii * kNumThreadsPerBlock + threadIdx.x];
+    topKIndices[ii] = smemIndices[ii * kNumThreadsPerBlock + threadIdx.x];
+  }
+
+  // Sort the elements.
+  TopKSort(smemFinal.topKSort)
+      .SortDescendingBlockedToStriped(topKLogits, topKIndices);
+
 // Store to global memory.
 #pragma unroll
   for (int ii = 0; ii < kNumTopKItemsPerThread; ++ii) {
     int offset = rowIdx * kTopK + ii * kNumThreadsPerBlock + threadIdx.x;
-    outIndices[offset] =
-        smemIndices[ii * kNumThreadsPerBlock + threadIdx.x] - rowStart;
+    outIndices[offset] = topKIndices[ii] - rowStart;
+    outLogits[offset] = topKLogits[ii];
   }
 }
 
-template <int kNumThreadsPerBlock = 512>
-static __global__ void topKPerRow(const float* logits, const int* rowStarts,
-                                  const int* rowEnds, int* outIndices,
-                                  int stride0, int stride1) {
-  // The number of bins in the histogram.
-  static constexpr int kNumBins = 512;
-
-  // The top-k width.
-  static constexpr int kTopK = 2048;
-
-  // The row computed by this block.
-  int rowIdx = blockIdx.x;
-
-  // The range of logits within the row.
-  int rowStart = rowStarts[rowIdx];
-  int rowEnd = rowEnds[rowIdx];
-
-  topKPerRowJob<kNumThreadsPerBlock, kNumBins, kTopK>(
-      logits, rowStart, rowEnd, rowIdx, outIndices, stride0, stride1);
-}
-
-template <int kNumThreadsPerBlock = 512>
-static __global__ void topKPerRowDecode(const float* logits, const int* seqLens,
-                                        int* outIndices, int stride0,
-                                        int stride1, int next_n) {
-  // The number of bins in the histogram.
-  static constexpr int kNumBins = 512;
-
-  // The top-k width.
-  static constexpr int kTopK = 2048;
-
-  // The row computed by this block.
-  int rowIdx = blockIdx.x;
-
-  // The range of logits within the row.
-  int rowStart = 0;
-  int seq_len = seqLens[rowIdx / next_n];
-  int rowEnd = seq_len - next_n + (rowIdx % next_n) + 1;
-
-  topKPerRowJob<kNumThreadsPerBlock, kNumBins, kTopK>(
-      logits, rowStart, rowEnd, rowIdx, outIndices, stride0, stride1);
-}
-
 }  // namespace vllm
 
 void apply_repetition_penalties_(
@@ -337,23 +326,10 @@ void apply_repetition_penalties_(
       });
 }
 
-void top_k_per_row_decode(const torch::Tensor& logits, int64_t next_n,
-                          const torch::Tensor& seqLens, torch::Tensor& indices,
-                          int64_t numRows, int64_t stride0, int64_t stride1) {
-  // Compute the results on the device.
-  constexpr int kNumThreadsPerBlock = 512;
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-
-  vllm::topKPerRowDecode<kNumThreadsPerBlock>
-      <<<numRows, kNumThreadsPerBlock, 0, stream>>>(
-          logits.data_ptr<float>(), seqLens.data_ptr<int>(),
-          indices.data_ptr<int>(), static_cast<int>(stride0),
-          static_cast<int>(stride1), static_cast<int>(next_n));
-}
-
 void top_k_per_row(const torch::Tensor& logits, const torch::Tensor& rowStarts,
                    const torch::Tensor& rowEnds, torch::Tensor& indices,
-                   int64_t numRows, int64_t stride0, int64_t stride1) {
+                   torch::Tensor& values, int64_t numRows, int64_t stride0,
+                   int64_t stride1) {
   // Compute the results on the device.
   constexpr int kNumThreadsPerBlock = 512;
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
@@ -362,5 +338,6 @@ void top_k_per_row(const torch::Tensor& logits, const torch::Tensor& rowStarts,
       <<<numRows, kNumThreadsPerBlock, 0, stream>>>(
           logits.data_ptr<float>(), rowStarts.data_ptr<int>(),
           rowEnds.data_ptr<int>(), indices.data_ptr<int>(),
-          static_cast<int>(stride0), static_cast<int>(stride1));
+          values.data_ptr<float>(), static_cast<int>(stride0),
+          static_cast<int>(stride1));
 }

csrc/torch_bindings.cpp

@@ -175,6 +175,12 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "float epsilon) -> ()");
   ops.impl("fused_add_rms_norm", torch::kCUDA, &fused_add_rms_norm);
 
+  // Polynomial Normalization.
+  ops.def(
+      "poly_norm(Tensor! out, Tensor input, Tensor weight, Tensor bias, float "
+      "epsilon) -> ()");
+  ops.impl("poly_norm", torch::kCUDA, &poly_norm);
+
   // Apply repetition penalties to logits in-place
   ops.def(
       "apply_repetition_penalties_(Tensor! logits, Tensor prompt_mask, "
@@ -185,16 +191,10 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   // Optimized top-k per row operation
   ops.def(
       "top_k_per_row(Tensor logits, Tensor rowStarts, Tensor rowEnds, "
-      "Tensor! indices, int numRows, int stride0, "
+      "Tensor! indices, Tensor! values, int numRows, int stride0, "
       "int stride1) -> ()");
   ops.impl("top_k_per_row", torch::kCUDA, &top_k_per_row);
 
-  ops.def(
-      "top_k_per_row_decode(Tensor logits, int next_n, "
-      "Tensor seq_lens, Tensor! indices, int numRows, "
-      "int stride0, int stride1) -> ()");
-  ops.impl("top_k_per_row_decode", torch::kCUDA, &top_k_per_row_decode);
-
   // Layernorm-quant
   // Apply Root Mean Square (RMS) Normalization to the input tensor.
   ops.def(
@@ -557,8 +557,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   // to prevent the meta function registry.
   ops.def(
       "gptq_gemm(Tensor a, Tensor b_q_weight, Tensor b_gptq_qzeros, "
-      "Tensor b_gptq_scales, Tensor b_g_idx, bool use_exllama, bool "
-      "use_v2_format, int bit) "
+      "Tensor b_gptq_scales, Tensor b_g_idx, bool use_exllama, int bit) "
       "-> Tensor",
       {stride_tag});
   ops.impl("gptq_gemm", torch::kCUDA, &gptq_gemm);

docker/Dockerfile

@@ -5,7 +5,7 @@
 # docs/contributing/dockerfile/dockerfile.md and
 # docs/assets/contributing/dockerfile-stages-dependency.png
 
-ARG CUDA_VERSION=12.9.1
+ARG CUDA_VERSION=12.8.1
 ARG PYTHON_VERSION=3.12
 
 # By parameterizing the base images, we allow third-party to use their own
@@ -132,9 +132,7 @@ WORKDIR /workspace
 COPY requirements/common.txt requirements/common.txt
 COPY requirements/cuda.txt requirements/cuda.txt
 RUN --mount=type=cache,target=/root/.cache/uv \
-    # TODO: remove apache-tvm-ffi once FlashInfer is fixed https://github.com/flashinfer-ai/flashinfer/issues/1962
-    uv pip install --python /opt/venv/bin/python3 --pre apache-tvm-ffi==0.1.0b15 \
-    && uv pip install --python /opt/venv/bin/python3 -r requirements/cuda.txt \
+    uv pip install --python /opt/venv/bin/python3 -r requirements/cuda.txt \
     --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
 
 # cuda arch list used by torch
@@ -275,7 +273,6 @@ WORKDIR /vllm-workspace
 ENV DEBIAN_FRONTEND=noninteractive
 ARG TARGETPLATFORM
 
-# TODO (huydhn): There is no prebuilt gdrcopy package on 12.9 at the moment
 ARG GDRCOPY_CUDA_VERSION=12.8
 # Keep in line with FINAL_BASE_IMAGE
 ARG GDRCOPY_OS_VERSION=Ubuntu22_04
@@ -356,23 +353,14 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 # Install vllm wheel first, so that torch etc will be installed.
 RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
     --mount=type=cache,target=/root/.cache/uv \
-    # TODO: remove apache-tvm-ffi once FlashInfer is fixed https://github.com/flashinfer-ai/flashinfer/issues/1962
-    uv pip install --system --pre apache-tvm-ffi==0.1.0b15 \
-    && uv pip install --system dist/*.whl --verbose \
+    uv pip install --system dist/*.whl --verbose \
         --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
 
-# TODO (huydhn): Remove this once xformers is released for 2.9.0
-RUN --mount=type=cache,target=/root/.cache/uv bash - <<'BASH'
-    . /etc/environment
-    export TORCH_CUDA_ARCH_LIST='7.5 8.0+PTX 9.0a'
-    uv pip install --system --no-build-isolation "git+https://github.com/facebookresearch/xformers@v0.0.32.post2"
-BASH
-
 # Install FlashInfer pre-compiled kernel cache and binaries
 # https://docs.flashinfer.ai/installation.html
 RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system flashinfer-cubin==0.4.1 \
-    && uv pip install --system flashinfer-jit-cache==0.4.1 \
+    uv pip install --system flashinfer-cubin==0.4.0 \
+    && uv pip install --system flashinfer-jit-cache==0.4.0 \
         --extra-index-url https://flashinfer.ai/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') \
     && flashinfer show-config
 
@@ -434,7 +422,6 @@ ARG PYTHON_VERSION
 
 ARG PIP_INDEX_URL UV_INDEX_URL
 ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
-ARG PYTORCH_CUDA_INDEX_BASE_URL
 
 # This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
 # Reference: https://github.com/astral-sh/uv/pull/1694
@@ -447,8 +434,7 @@ ENV UV_LINK_MODE=copy
 RUN --mount=type=cache,target=/root/.cache/uv \
     CUDA_MAJOR="${CUDA_VERSION%%.*}"; \
     if [ "$CUDA_MAJOR" -ge 12 ]; then \
-        uv pip install --system -r requirements/dev.txt \
-        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
+        uv pip install --system -r requirements/dev.txt; \
     fi
 
 # install development dependencies (for testing)

docker/Dockerfile.cpu

@@ -31,7 +31,7 @@ ARG PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu"
 RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
     --mount=type=cache,target=/var/lib/apt,sharing=locked \
     apt-get update -y \
-    && apt-get install -y --no-install-recommends sudo ccache git curl wget ca-certificates \
+    && apt-get install -y --no-install-recommends ccache git curl wget ca-certificates \
         gcc-12 g++-12 libtcmalloc-minimal4 libnuma-dev ffmpeg libsm6 libxext6 libgl1 jq lsof \
     && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /usr/bin/g++ g++ /usr/bin/g++-12 \
     && curl -LsSf https://astral.sh/uv/install.sh | sh
@@ -79,9 +79,6 @@ RUN echo 'ulimit -c 0' >> ~/.bashrc
 ######################### BUILD IMAGE #########################
 FROM base AS vllm-build
 
-ARG max_jobs=32
-ENV MAX_JOBS=${max_jobs}
-
 ARG GIT_REPO_CHECK=0
 # Support for building with non-AVX512 vLLM: docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" ...
 ARG VLLM_CPU_DISABLE_AVX512=0
@@ -107,20 +104,16 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     --mount=type=cache,target=/root/.cache/ccache \
     --mount=type=cache,target=/workspace/vllm/.deps,sharing=locked \
     --mount=type=bind,source=.git,target=.git \
-    VLLM_TARGET_DEVICE=cpu python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38
+    VLLM_TARGET_DEVICE=cpu python3 setup.py bdist_wheel
 
 ######################### TEST DEPS #########################
 FROM base AS vllm-test-deps
 
 WORKDIR /workspace/vllm
 
-# TODO: Update to 2.9.0 when there is a new build for intel_extension_for_pytorch for that version
 RUN --mount=type=bind,src=requirements/test.in,target=requirements/test.in \
     cp requirements/test.in requirements/cpu-test.in && \
     sed -i '/mamba_ssm/d' requirements/cpu-test.in && \
-    sed -i 's/^torch==.*/torch==2.8.0/g' requirements/cpu-test.in && \
-    sed -i 's/torchaudio.*/torchaudio/g' requirements/cpu-test.in && \
-    sed -i 's/torchvision.*/torchvision/g' requirements/cpu-test.in && \
     uv pip compile requirements/cpu-test.in -o requirements/cpu-test.txt --index-strategy unsafe-best-match --torch-backend cpu
 
 RUN --mount=type=cache,target=/root/.cache/uv \

docker/Dockerfile.nightly_torch

@@ -246,7 +246,7 @@ RUN pip install setuptools==75.6.0 packaging==23.2 ninja==1.11.1.3 build==1.2.2.
 
 
 # build flashinfer for torch nightly from source around 10 mins
-# release version: v0.4.1
+# release version: v0.4.0
 # todo(elainewy): cache flashinfer build result for faster build
 ENV CCACHE_DIR=/root/.cache/ccache
 RUN --mount=type=cache,target=/root/.cache/ccache \
@@ -254,7 +254,7 @@ RUN --mount=type=cache,target=/root/.cache/ccache \
     echo "git clone flashinfer..." \
     && git clone --recursive https://github.com/flashinfer-ai/flashinfer.git \
     && cd flashinfer \
-    && git checkout v0.4.1\
+    && git checkout v0.4.0 \
     && git submodule update --init --recursive \
     && echo "finish git clone flashinfer..." \
     && rm -rf build \

docker/Dockerfile.rocm

@@ -12,7 +12,7 @@ ENV PYTORCH_ROCM_ARCH=${ARG_PYTORCH_ROCM_ARCH:-${PYTORCH_ROCM_ARCH}}
 RUN apt-get update -q -y && apt-get install -q -y \
     sqlite3 libsqlite3-dev libfmt-dev libmsgpack-dev libsuitesparse-dev \
     apt-transport-https ca-certificates wget curl
-# Remove sccache
+# Remove sccache    
 RUN python3 -m pip install --upgrade pip
 RUN apt-get purge -y sccache; python3 -m pip uninstall -y sccache; rm -f "$(which sccache)"
 ARG COMMON_WORKDIR

docker/Dockerfile.rocm_base

@@ -1,13 +1,13 @@
 ARG BASE_IMAGE=rocm/dev-ubuntu-22.04:7.0-complete
-ARG TRITON_BRANCH="57c693b6"
+ARG TRITON_BRANCH="f9e5bf54"
 ARG TRITON_REPO="https://github.com/ROCm/triton.git"
-ARG PYTORCH_BRANCH="1c57644d"
+ARG PYTORCH_BRANCH="b2fb6885"
 ARG PYTORCH_VISION_BRANCH="v0.23.0"
 ARG PYTORCH_REPO="https://github.com/ROCm/pytorch.git"
 ARG PYTORCH_VISION_REPO="https://github.com/pytorch/vision.git"
 ARG FA_BRANCH="0e60e394"
 ARG FA_REPO="https://github.com/Dao-AILab/flash-attention.git"
-ARG AITER_BRANCH="9716b1b8"
+ARG AITER_BRANCH="2ab9f4cd"
 ARG AITER_REPO="https://github.com/ROCm/aiter.git"
 
 FROM ${BASE_IMAGE} AS base

docs/api/README.md

@@ -20,6 +20,8 @@ API documentation for vLLM's configuration classes.
 - [vllm.config.CompilationConfig][]
 - [vllm.config.VllmConfig][]
 
+[](){ #offline-inference-api }
+
 ## Offline Inference
 
 LLM Class.
@@ -43,14 +45,18 @@ Engine classes for offline and online inference.
 
 Inference parameters for vLLM APIs.
 
+[](){ #sampling-params }
+
 - [vllm.SamplingParams][]
 - [vllm.PoolingParams][]
 
+[](){ #multi-modality }
+
 ## Multi-Modality
 
 vLLM provides experimental support for multi-modal models through the [vllm.multimodal][] package.
 
-Multi-modal inputs can be passed alongside text and token prompts to [supported models](../models/supported_models.md#list-of-multimodal-language-models)
+Multi-modal inputs can be passed alongside text and token prompts to [supported models][supported-mm-models]
 via the `multi_modal_data` field in [vllm.inputs.PromptType][].
 
 Looking to add your own multi-modal model? Please follow the instructions listed [here](../contributing/model/multimodal.md).

docs/configuration/README.md

@@ -4,6 +4,6 @@ This section lists the most common options for running vLLM.
 
 There are three main levels of configuration, from highest priority to lowest priority:
 
-- [Request parameters](../serving/openai_compatible_server.md#completions-api) and [input arguments](../api/README.md#inference-parameters)
+- [Request parameters][completions-api] and [input arguments][sampling-params]
 - [Engine arguments](./engine_args.md)
 - [Environment variables](./env_vars.md)

docs/configuration/conserving_memory.md

@@ -23,7 +23,7 @@ llm = LLM(model="ibm-granite/granite-3.1-8b-instruct", tensor_parallel_size=2)
 !!! note
     With tensor parallelism enabled, each process will read the whole model and split it into chunks, which makes the disk reading time even longer (proportional to the size of tensor parallelism).
 
-    You can convert the model checkpoint to a sharded checkpoint using [examples/offline_inference/save_sharded_state.py](../../examples/offline_inference/save_sharded_state.py). The conversion process might take some time, but later you can load the sharded checkpoint much faster. The model loading time should remain constant regardless of the size of tensor parallelism.
+    You can convert the model checkpoint to a sharded checkpoint using <gh-file:examples/offline_inference/save_sharded_state.py>. The conversion process might take some time, but later you can load the sharded checkpoint much faster. The model loading time should remain constant regardless of the size of tensor parallelism.
 
 ## Quantization
 

docs/configuration/optimization.md

@@ -27,6 +27,8 @@ You can monitor the number of preemption requests through Prometheus metrics exp
 
 In vLLM V1, the default preemption mode is `RECOMPUTE` rather than `SWAP`, as recomputation has lower overhead in the V1 architecture.
 
+[](){ #chunked-prefill }
+
 ## Chunked Prefill
 
 Chunked prefill allows vLLM to process large prefills in smaller chunks and batch them together with decode requests. This feature helps improve both throughput and latency by better balancing compute-bound (prefill) and memory-bound (decode) operations.
@@ -172,14 +174,14 @@ Regardless, you need to set `mm_encoder_tp_mode="data"` in engine arguments to u
 
 Known supported models (with corresponding benchmarks):
 
-- dots_ocr (<https://github.com/vllm-project/vllm/pull/25466>)
-- GLM-4.1V or above (<https://github.com/vllm-project/vllm/pull/23168>)
-- InternVL (<https://github.com/vllm-project/vllm/pull/23909>)
-- Kimi-VL (<https://github.com/vllm-project/vllm/pull/23817>)
-- Llama4 (<https://github.com/vllm-project/vllm/pull/18368>)
-- MiniCPM-V-2.5 or above (<https://github.com/vllm-project/vllm/pull/23327>, <https://github.com/vllm-project/vllm/pull/23948>)
-- Qwen2-VL or above (<https://github.com/vllm-project/vllm/pull/22742>, <https://github.com/vllm-project/vllm/pull/24955>, <https://github.com/vllm-project/vllm/pull/25445>)
-- Step3 (<https://github.com/vllm-project/vllm/pull/22697>)
+- dots_ocr (<gh-pr:25466>)
+- GLM-4.1V or above (<gh-pr:23168>)
+- InternVL (<gh-pr:23909>)
+- Kimi-VL (<gh-pr:23817>)
+- Llama4 (<gh-pr:18368>)
+- MiniCPM-V-2.5 or above (<gh-pr:23327>, <gh-pr:23948>)
+- Qwen2-VL or above (<gh-pr:22742>, <gh-pr:24955>, <gh-pr:25445>)
+- Step3 (<gh-pr:22697>)
 
 ## Input Processing
 

docs/configuration/tpu.md

@@ -96,7 +96,7 @@ Although it’s common to do this with GPUs, don't try to fragment 2 or 8 differ
 
 ### Tune your workloads
 
-Although we try to have great default configs, we strongly recommend you check out the [vLLM auto-tuner](../../benchmarks/auto_tune/README.md) to optimize your workloads for your use case.
+Although we try to have great default configs, we strongly recommend you check out the [vLLM auto-tuner](gh-file:benchmarks/auto_tune/README.md) to optimize your workloads for your use case.
 
 ### Future Topics We'll Cover
 

docs/contributing/README.md

@@ -16,13 +16,13 @@ Finally, one of the most impactful ways to support us is by raising awareness ab
 Unsure on where to start? Check out the following links for tasks to work on:
 
 - [Good first issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue%20state%3Aopen%20label%3A%22good%20first%20issue%22)
-    - [Selected onboarding tasks](https://github.com/orgs/vllm-project/projects/6)
+    - [Selected onboarding tasks](gh-project:6)
 - [New model requests](https://github.com/vllm-project/vllm/issues?q=is%3Aissue%20state%3Aopen%20label%3A%22new-model%22)
-    - [Models with multi-modal capabilities](https://github.com/orgs/vllm-project/projects/10)
+    - [Models with multi-modal capabilities](gh-project:10)
 
 ## License
 
-See [LICENSE](../../LICENSE).
+See <gh-file:LICENSE>.
 
 ## Developing
 
@@ -54,7 +54,7 @@ For more details about installing from source and installing for other hardware,
 For an optimized workflow when iterating on C++/CUDA kernels, see the [Incremental Compilation Workflow](./incremental_build.md) for recommendations.
 
 !!! tip
-    vLLM is compatible with Python versions 3.10 to 3.13. However, vLLM's default [Dockerfile](../../docker/Dockerfile) ships with Python 3.12 and tests in CI (except `mypy`) are run with Python 3.12.
+    vLLM is compatible with Python versions 3.10 to 3.13. However, vLLM's default [Dockerfile](gh-file:docker/Dockerfile) ships with Python 3.12 and tests in CI (except `mypy`) are run with Python 3.12.
 
     Therefore, we recommend developing with Python 3.12 to minimise the chance of your local environment clashing with our CI environment.
 
@@ -88,7 +88,7 @@ vLLM's `pre-commit` hooks will now run automatically every time you commit.
 
 ### Documentation
 
-MkDocs is a fast, simple and downright gorgeous static site generator that's geared towards building project documentation. Documentation source files are written in Markdown, and configured with a single YAML configuration file, [mkdocs.yaml](../../mkdocs.yaml).
+MkDocs is a fast, simple and downright gorgeous static site generator that's geared towards building project documentation. Documentation source files are written in Markdown, and configured with a single YAML configuration file, <gh-file:mkdocs.yaml>.
 
 Get started with:
 
@@ -152,7 +152,7 @@ pytest -s -v tests/test_logger.py
 If you encounter a bug or have a feature request, please [search existing issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue) first to see if it has already been reported. If not, please [file a new issue](https://github.com/vllm-project/vllm/issues/new/choose), providing as much relevant information as possible.
 
 !!! important
-    If you discover a security vulnerability, please follow the instructions [here](../../SECURITY.md).
+    If you discover a security vulnerability, please follow the instructions [here](gh-file:SECURITY.md#reporting-a-vulnerability).
 
 ## Pull Requests & Code Reviews
 
@@ -162,7 +162,7 @@ code quality and improve the efficiency of the review process.
 
 ### DCO and Signed-off-by
 
-When contributing changes to this project, you must agree to the [DCO](../../DCO).
+When contributing changes to this project, you must agree to the <gh-file:DCO>.
 Commits must include a `Signed-off-by:` header which certifies agreement with
 the terms of the DCO.
 

docs/contributing/benchmarks.md

@@ -6,10 +6,9 @@ toc_depth: 4
 
 vLLM provides comprehensive benchmarking tools for performance testing and evaluation:
 
-- **[Benchmark CLI](#benchmark-cli)**: `vllm bench` CLI tools and specialized benchmark scripts for interactive performance testing
-- **[Parameter sweeps](#parameter-sweeps)**: Automate `vllm bench` runs for multiple configurations
-- **[Performance benchmarks](#performance-benchmarks)**: Automated CI benchmarks for development
-- **[Nightly benchmarks](#nightly-benchmarks)**: Comparative benchmarks against alternatives
+- **[Benchmark CLI]**: `vllm bench` CLI tools and specialized benchmark scripts for interactive performance testing
+- **[Performance benchmarks][performance-benchmarks]**: Automated CI benchmarks for development
+- **[Nightly benchmarks][nightly-benchmarks]**: Comparative benchmarks against alternatives
 
 [Benchmark CLI]: #benchmark-cli
 
@@ -30,7 +29,7 @@ th {
 | Dataset | Online | Offline | Data Path |
 |---------|--------|---------|-----------|
 | ShareGPT | ✅ | ✅ | `wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json` |
-| ShareGPT4V (Image) | ✅ | ✅ | `wget https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/resolve/main/sharegpt4v_instruct_gpt4-vision_cap100k.json`<br>Note that the images need to be downloaded separately. For example, to download COCO's 2017 Train images:<br>`wget http://images.cocodataset.org/zips/train2017.zip` |
+| ShareGPT4V (Image) | ✅ | ✅ | `wget https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/blob/main/sharegpt4v_instruct_gpt4-vision_cap100k.json`<br>Note that the images need to be downloaded separately. For example, to download COCO's 2017 Train images:<br>`wget http://images.cocodataset.org/zips/train2017.zip` |
 | ShareGPT4Video (Video) | ✅ | ✅ | `git clone https://huggingface.co/datasets/ShareGPT4Video/ShareGPT4Video` |
 | BurstGPT | ✅ | ✅ | `wget https://github.com/HPMLL/BurstGPT/releases/download/v1.1/BurstGPT_without_fails_2.csv` |
 | Sonnet (deprecated) | ✅ | ✅ | Local file: `benchmarks/sonnet.txt` |
@@ -321,73 +320,6 @@ The following arguments can be used to control the ramp-up:
 - `--ramp-up-start-rps`: The request rate at the beginning of the benchmark.
 - `--ramp-up-end-rps`: The request rate at the end of the benchmark.
 
-##### Load Pattern Configuration
-
-vLLM's benchmark serving script provides sophisticated load pattern simulation capabilities through three key parameters that control request generation and concurrency behavior:
-
-###### Load Pattern Control Parameters
-
-- `--request-rate`: Controls the target request generation rate (requests per second). Set to `inf` for maximum throughput testing or finite values for controlled load simulation.
-- `--burstiness`: Controls traffic variability using a Gamma distribution (range: > 0). Lower values create bursty traffic, higher values create uniform traffic.
-- `--max-concurrency`: Limits concurrent outstanding requests. If this argument is not provided, concurrency is unlimited. Set a value to simulate backpressure.
-
-These parameters work together to create realistic load patterns with carefully chosen defaults. The `--request-rate` parameter defaults to `inf` (infinite), which sends all requests immediately for maximum throughput testing. When set to finite values, it uses either a Poisson process (default `--burstiness=1.0`) or Gamma distribution for realistic request timing. The `--burstiness` parameter only takes effect when `--request-rate` is not infinite - a value of 1.0 creates natural Poisson traffic, while lower values (0.1-0.5) create bursty patterns and higher values (2.0-5.0) create uniform spacing. The `--max-concurrency` parameter defaults to `None` (unlimited) but can be set to simulate real-world constraints where a load balancer or API gateway limits concurrent connections. When combined, these parameters allow you to simulate everything from unrestricted stress testing (`--request-rate=inf`) to production-like scenarios with realistic arrival patterns and resource constraints.
-
-The `--burstiness` parameter mathematically controls request arrival patterns using a Gamma distribution where:
-
-- Shape parameter: `burstiness` value
-- Coefficient of Variation (CV): $\frac{1}{\sqrt{burstiness}}$
-- Traffic characteristics:
-    - `burstiness = 0.1`: Highly bursty traffic (CV ≈ 3.16) - stress testing
-    - `burstiness = 1.0`: Natural Poisson traffic (CV = 1.0) - realistic simulation  
-    - `burstiness = 5.0`: Uniform traffic (CV ≈ 0.45) - controlled load testing
-
-![Load Pattern Examples](../assets/contributing/load-pattern-examples.png)
-
-*Figure: Load pattern examples for each use case. Top row: Request arrival timelines showing cumulative requests over time. Bottom row: Inter-arrival time distributions showing traffic variability patterns. Each column represents a different use case with its specific parameter settings and resulting traffic characteristics.*
-
-Load Pattern Recommendations by Use Case:
-
-| Use Case           | Burstiness   | Request Rate    | Max Concurrency | Description                                               |
-| ---                | ---          | ---             | ---             | ---                                                       |
-| Maximum Throughput | N/A          | Infinite        | Limited         | **Most common**: Simulates load balancer/gateway limits with unlimited user demand |
-| Realistic Testing  | 1.0          | Moderate (5-20) | Infinite        | Natural Poisson traffic patterns for baseline performance |
-| Stress Testing     | 0.1-0.5      | High (20-100)   | Infinite        | Challenging burst patterns to test resilience             |
-| Latency Profiling  | 2.0-5.0      | Low (1-10)      | Infinite        | Uniform load for consistent timing analysis               |
-| Capacity Planning  | 1.0          | Variable        | Limited         | Test resource limits with realistic constraints           |
-| SLA Validation     | 1.0          | Target rate     | SLA limit       | Production-like constraints for compliance testing        |
-
-These load patterns help evaluate different aspects of your vLLM deployment, from basic performance characteristics to resilience under challenging traffic conditions.
-
-The **Maximum Throughput** pattern (`--request-rate=inf --max-concurrency=<limit>`) is the most commonly used configuration for production benchmarking. This simulates real-world deployment architectures where:
-
-- Users send requests as fast as they can (infinite rate)
-- A load balancer or API gateway controls the maximum concurrent connections
-- The system operates at its concurrency limit, revealing true throughput capacity
-- `--burstiness` has no effect since request timing is not controlled when rate is infinite
-
-This pattern helps determine optimal concurrency settings for your production load balancer configuration.
-
-To effectively configure load patterns, especially for **Capacity Planning** and **SLA Validation** use cases, you need to understand your system's resource limits. During startup, vLLM reports KV cache configuration that directly impacts your load testing parameters:
-
-```text
-GPU KV cache size: 15,728,640 tokens
-Maximum concurrency for 8,192 tokens per request: 1920
-```
-
-Where:
-
-- GPU KV cache size: Total tokens that can be cached across all concurrent requests
-- Maximum concurrency: Theoretical maximum concurrent requests for the given `max_model_len`
-- Calculation: `max_concurrency = kv_cache_size / max_model_len`
-
-Using KV cache metrics for load pattern configuration:
-
-- For Capacity Planning: Set `--max-concurrency` to 80-90% of the reported maximum to test realistic resource constraints
-- For SLA Validation: Use the reported maximum as your SLA limit to ensure compliance testing matches production capacity
-- For Realistic Testing: Monitor memory usage when approaching theoretical limits to understand sustainable request rates
-- Request rate guidance: Use the KV cache size to estimate sustainable request rates for your specific workload and sequence lengths
-
 </details>
 
 #### 📈 Offline Throughput Benchmark
@@ -782,7 +714,7 @@ Generate synthetic image inputs alongside random text prompts to stress-test vis
 
 Notes:
 
-- Works only with online benchmark via the OpenAI backend (`--backend openai-chat`) and endpoint `/v1/chat/completions`.
+- Works only with online benchmark via the OpenAI  backend (`--backend openai-chat`) and endpoint `/v1/chat/completions`.
 - Video sampling is not yet implemented.
 
 Start the server (example):
@@ -890,7 +822,7 @@ you should set `--endpoint /v1/embeddings` to use the Embeddings API. The backen
 - CLIP: `--backend openai-embeddings-clip`
 - VLM2Vec: `--backend openai-embeddings-vlm2vec`
 
-For other models, please add your own implementation inside [vllm/benchmarks/lib/endpoint_request_func.py](../../vllm/benchmarks/lib/endpoint_request_func.py) to match the expected instruction format.
+For other models, please add your own implementation inside <gh-file:vllm/benchmarks/lib/endpoint_request_func.py> to match the expected instruction format.
 
 You can use any text or multi-modal dataset to benchmark the model, as long as the model supports it.
 For example, you can use ShareGPT and VisionArena to benchmark vision-language embeddings.
@@ -992,162 +924,7 @@ throughput numbers correctly is also adjusted.
 
 </details>
 
-## Parameter Sweeps
-
-### Online Benchmark
-
-[`vllm/benchmarks/sweep/serve.py`](../../vllm/benchmarks/sweep/serve.py) automatically starts `vllm serve` and runs `vllm bench serve` to evaluate vLLM over multiple configurations.
-
-Follow these steps to run the script:
-
-1. Construct the base command to `vllm serve`, and pass it to the `--serve-cmd` option.
-2. Construct the base command to `vllm bench serve`, and pass it to the `--bench-cmd` option.
-3. (Optional) If you would like to vary the settings of `vllm serve`, create a new JSON file and populate it with the parameter combinations you want to test. Pass the file path to `--serve-params`.
-
-    - Example: Tuning `--max-num-seqs` and `--max-num-batched-tokens`:
-
-    ```json
-    [
-        {
-            "max_num_seqs": 32,
-            "max_num_batched_tokens": 1024
-        },
-        {
-            "max_num_seqs": 64,
-            "max_num_batched_tokens": 1024
-        },
-        {
-            "max_num_seqs": 64,
-            "max_num_batched_tokens": 2048
-        },
-        {
-            "max_num_seqs": 128,
-            "max_num_batched_tokens": 2048
-        },
-        {
-            "max_num_seqs": 128,
-            "max_num_batched_tokens": 4096
-        },
-        {
-            "max_num_seqs": 256,
-            "max_num_batched_tokens": 4096
-        }
-    ]
-    ```
-
-4. (Optional) If you would like to vary the settings of `vllm bench serve`, create a new JSON file and populate it with the parameter combinations you want to test. Pass the file path to `--bench-params`.
-
-    - Example: Using different input/output lengths for random dataset:
-
-    ```json
-    [
-        {
-            "random_input_len": 128,
-            "random_output_len": 32
-        },
-        {
-            "random_input_len": 256,
-            "random_output_len": 64
-        },
-        {
-            "random_input_len": 512,
-            "random_output_len": 128
-        }
-    ]
-    ```
-
-5. Determine where you want to save the results, and pass that to `--output-dir`.
-
-Example command:
-
-```bash
-python -m vllm.benchmarks.sweep.serve \
-    --serve-cmd 'vllm serve meta-llama/Llama-2-7b-chat-hf' \
-    --bench-cmd 'vllm bench serve --model meta-llama/Llama-2-7b-chat-hf --backend vllm --endpoint /v1/completions --dataset-name sharegpt --dataset-path benchmarks/ShareGPT_V3_unfiltered_cleaned_split.json' \
-    --serve-params benchmarks/serve_hparams.json \
-    --bench-params benchmarks/bench_hparams.json \
-    -o benchmarks/results
-```
-
-!!! important
-    If both `--serve-params` and `--bench-params` are passed, the script will iterate over the Cartesian product between them.
-    You can use `--dry-run` to preview the commands to be run.
-
-    We only start the server once for each `--serve-params`, and keep it running for multiple `--bench-params`.
-    Between each benchmark run, we call the `/reset_prefix_cache` and `/reset_mm_cache` endpoints to get a clean slate for the next run.
-    In case you are using a custom `--serve-cmd`, you can override the commands used for resetting the state by setting `--after-bench-cmd`.
-
-!!! note
-    By default, each parameter combination is run 3 times to make the results more reliable. You can adjust the number of runs by setting `--num-runs`.
-
-!!! tip
-    You can use the `--resume` option to continue the parameter sweep if one of the runs failed.
-  
-### SLA Auto-Tuner
-
-[`vllm/benchmarks/sweep/serve_sla.py`](../../vllm/benchmarks/sweep/serve_sla.py) is a wrapper over [`vllm/benchmarks/sweep/serve.py`](../../vllm/benchmarks/sweep/serve.py) that tunes either the request rate or concurrency (choose using `--sla-variable`) in order to satisfy the SLA constraints given by `--sla-params`.
-
-For example, to ensure E2E latency within different target values for 99% of requests:
-
-```json
-[
-    {
-        "p99_e2el_ms": "<=200"
-    },
-    {
-        "p99_e2el_ms": "<=500"
-    },
-    {
-        "p99_e2el_ms": "<=1000"
-    },
-    {
-        "p99_e2el_ms": "<=2000"
-    }
-]
-```
-
-Example command:
-
-```bash
-python -m vllm.benchmarks.sweep.serve_sla \
-    --serve-cmd 'vllm serve meta-llama/Llama-2-7b-chat-hf' \
-    --bench-cmd 'vllm bench serve --model meta-llama/Llama-2-7b-chat-hf --backend vllm --endpoint /v1/completions --dataset-name sharegpt --dataset-path benchmarks/ShareGPT_V3_unfiltered_cleaned_split.json' \
-    --serve-params benchmarks/serve_hparams.json \
-    --bench-params benchmarks/bench_hparams.json \
-    --sla-params benchmarks/sla_hparams.json \
-    --sla-variable max_concurrency \
-    -o benchmarks/results
-```
-
-The algorithm for adjusting the SLA variable is as follows:
-
-1. Run the benchmark with infinite QPS, and use the corresponding metrics to determine the initial value of the variable.
-    - For example, the initial request rate is set to the concurrency under infinite QPS.
-2. If the SLA is still satisfied, keep doubling the value until the SLA is no longer satisfied. This gives a relatively narrow window that contains the point where the SLA is barely satisfied.
-3. Apply binary search over the window to find the maximum value that still satisfies the SLA.
-
-!!! important
-    SLA tuning is applied over each combination of `--serve-params`, `--bench-params`, and `--sla-params`.
-
-    For a given combination of `--serve-params` and `--bench-params`, we share the benchmark results across `--sla-params` to avoid rerunning benchmarks with the same SLA variable value.
-
-### Visualizer
-
-[`vllm/benchmarks/sweep/plot.py`](../../vllm/benchmarks/sweep/plot.py) can be used to plot performance curves from parameter sweep results.
-
-Example command:
-
-```bash
-python -m vllm.benchmarks.sweep.plot benchmarks/results/<timestamp> \
-    --var-x max_concurrency \
-    --row-by random_input_len \
-    --col-by random_output_len \
-    --curve-by api_server_count,max_num_batched_tokens \
-    --filter-by 'max_concurrency<=1024'
-```
-
-!!! tip
-    You can use `--dry-run` to preview the figures to be plotted.
+[](){ #performance-benchmarks }
 
 ## Performance Benchmarks
 
@@ -1185,7 +962,7 @@ For more results visualization, check the [visualizing the results](https://gith
 
 The latest performance results are hosted on the public [vLLM Performance Dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm).
 
-More information on the performance benchmarks and their parameters can be found in [Benchmark README](https://github.com/intel-ai-tce/vllm/blob/more_cpu_models/.buildkite/nightly-benchmarks/README.md) and [performance benchmark description](../../.buildkite/nightly-benchmarks/performance-benchmarks-descriptions.md).
+More information on the performance benchmarks and their parameters can be found in [Benchmark README](https://github.com/intel-ai-tce/vllm/blob/more_cpu_models/.buildkite/nightly-benchmarks/README.md) and [performance benchmark description](gh-file:.buildkite/nightly-benchmarks/performance-benchmarks-descriptions.md).
 
 ### Continuous Benchmarking
 
@@ -1211,10 +988,12 @@ The benchmarking currently runs on a predefined set of models configured in the
 
 All continuous benchmarking results are automatically published to the public [vLLM Performance Dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm).
 
+[](){ #nightly-benchmarks }
+
 ## Nightly Benchmarks
 
 These compare vLLM's performance against alternatives (`tgi`, `trt-llm`, and `lmdeploy`) when there are major updates of vLLM (e.g., bumping up to a new version). They are primarily intended for consumers to evaluate when to choose vLLM over other options and are triggered on every commit with both the `perf-benchmarks` and `nightly-benchmarks` labels.
 
 The latest nightly benchmark results are shared in major release blog posts such as [vLLM v0.6.0](https://blog.vllm.ai/2024/09/05/perf-update.html).
 
-More information on the nightly benchmarks and their parameters can be found [here](../../.buildkite/nightly-benchmarks/nightly-descriptions.md).
+More information on the nightly benchmarks and their parameters can be found [here](gh-file:.buildkite/nightly-benchmarks/nightly-descriptions.md).

docs/contributing/ci/failures.md

@@ -64,7 +64,7 @@ Download the full log file from Buildkite locally.
 
 Strip timestamps and colorization:
 
-[.buildkite/scripts/ci-clean-log.sh](../../../.buildkite/scripts/ci-clean-log.sh)
+<gh-file:.buildkite/scripts/ci-clean-log.sh>
 
 ```bash
 ./ci-clean-log.sh ci.log
@@ -87,7 +87,7 @@ tail -525 ci_build.log | wl-copy
 
 CI test failures may be flaky. Use a bash loop to run repeatedly:
 
-[.buildkite/scripts/rerun-test.sh](../../../.buildkite/scripts/rerun-test.sh)
+<gh-file:.buildkite/scripts/rerun-test.sh>
 
 ```bash
 ./rerun-test.sh tests/v1/engine/test_engine_core_client.py::test_kv_cache_events[True-tcp]

docs/contributing/ci/update_pytorch_version.md

@@ -5,7 +5,7 @@ release in CI/CD. It is standard practice to submit a PR to update the
 PyTorch version as early as possible when a new [PyTorch stable
 release](https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-cadence) becomes available.
 This process is non-trivial due to the gap between PyTorch
-releases. Using <https://github.com/vllm-project/vllm/pull/16859> as an example, this document outlines common steps to achieve this
+releases. Using <gh-pr:16859> as an example, this document outlines common steps to achieve this
 update along with a list of potential issues and how to address them.
 
 ## Test PyTorch release candidates (RCs)
@@ -85,9 +85,9 @@ and timeout. Additionally, since vLLM's fastcheck pipeline runs in read-only mod
 it doesn't populate the cache, so re-running it to warm up the cache
 is ineffective.
 
-While ongoing efforts like <https://github.com/vllm-project/vllm/issues/17419>
+While ongoing efforts like [#17419](gh-issue:17419)
 address the long build time at its source, the current workaround is to set `VLLM_CI_BRANCH`
-to a custom branch provided by @khluu (`VLLM_CI_BRANCH=khluu/long_build`)
+to a custom branch provided by @khluu (`VLLM_CI_BRANCH=khluu/use_postmerge_q`)
 when manually triggering a build on Buildkite. This branch accomplishes two things:
 
 1. Increase the timeout limit to 10 hours so that the build doesn't time out.
@@ -100,17 +100,35 @@ to warm it up so that future builds are faster.
 
 ## Update dependencies
 
-Several vLLM dependencies like xFormers depend on PyTorch and need
+Several vLLM dependencies, such as FlashInfer, also depend on PyTorch and need
 to be updated accordingly. Rather than waiting for all of them to publish new
 releases (which would take too much time), they can be built from
 source to unblock the update process.
 
-### xFormers
+### FlashInfer
+
+Here is how to build and install it from source with `torch2.7.0+cu128` in vLLM [Dockerfile](https://github.com/vllm-project/vllm/blob/27bebcd89792d5c4b08af7a65095759526f2f9e1/docker/Dockerfile#L259-L271):
 
 ```bash
-export TORCH_CUDA_ARCH_LIST='7.5 8.0+PTX 9.0a'
+export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.9 9.0 10.0+PTX'
+export FLASHINFER_ENABLE_SM90=1
+uv pip install --system \
+    --no-build-isolation "git+https://github.com/flashinfer-ai/flashinfer@v0.2.6.post1"
+```
+
+One caveat is that building FlashInfer from source adds approximately 30
+minutes to the vLLM build time. Therefore, it's preferable to cache the wheel in a
+public location for immediate installation, such as [this FlashInfer wheel link](https://download.pytorch.org/whl/cu128/flashinfer/flashinfer_python-0.2.6.post1%2Bcu128torch2.7-cp39-abi3-linux_x86_64.whl). For future releases, contact the PyTorch release
+team if you want to get the package published there.
+
+### xFormers
+
+Similar to FlashInfer, here is how to build and install xFormers from source:
+
+```bash
+export TORCH_CUDA_ARCH_LIST='7.0 7.5 8.0 8.9 9.0 10.0+PTX'
 MAX_JOBS=16 uv pip install --system \
-    --no-build-isolation "git+https://github.com/facebookresearch/xformers@v0.0.32.post2"
+    --no-build-isolation "git+https://github.com/facebookresearch/xformers@v0.0.30"
 ```
 
 ## Update all the different vLLM platforms
@@ -120,5 +138,5 @@ to handle some platforms separately. The separation of requirements and Dockerfi
 for different platforms in vLLM CI/CD allows us to selectively choose
 which platforms to update. For instance, updating XPU requires the corresponding
 release from [Intel Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch) by Intel.
-While <https://github.com/vllm-project/vllm/pull/16859> updated vLLM to PyTorch 2.7.0 on CPU, CUDA, and ROCm,
-<https://github.com/vllm-project/vllm/pull/17444> completed the update for XPU.
+While <gh-pr:16859> updated vLLM to PyTorch 2.7.0 on CPU, CUDA, and ROCm,
+<gh-pr:17444> completed the update for XPU.

docs/contributing/dockerfile/dockerfile.md

@@ -1,6 +1,6 @@
 # Dockerfile
 
-We provide a [docker/Dockerfile](../../../docker/Dockerfile) to construct the image for running an OpenAI compatible server with vLLM.
+We provide a <gh-file:docker/Dockerfile> to construct the image for running an OpenAI compatible server with vLLM.
 More information about deploying with Docker can be found [here](../../deployment/docker.md).
 
 Below is a visual representation of the multi-stage Dockerfile. The build graph contains the following nodes:

docs/contributing/model/README.md

@@ -1,7 +1,7 @@
 # Summary
 
 !!! important
-    Many decoder language models can now be automatically loaded using the [Transformers backend](../../models/supported_models.md#transformers) without having to implement them in vLLM. See if `vllm serve <model>` works first!
+    Many decoder language models can now be automatically loaded using the [Transformers backend][transformers-backend] without having to implement them in vLLM. See if `vllm serve <model>` works first!
 
 vLLM models are specialized [PyTorch](https://pytorch.org/) models that take advantage of various [features](../../features/README.md#compatibility-matrix) to optimize their performance.
 

docs/contributing/model/basic.md

@@ -5,7 +5,7 @@ This guide walks you through the steps to implement a basic vLLM model.
 ## 1. Bring your model code
 
 First, clone the PyTorch model code from the source repository.
-For instance, vLLM's [OPT model](../../../vllm/model_executor/models/opt.py) was adapted from
+For instance, vLLM's [OPT model](gh-file:vllm/model_executor/models/opt.py) was adapted from
 HuggingFace's [modeling_opt.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/opt/modeling_opt.py) file.
 
 !!! warning
@@ -83,7 +83,7 @@ def forward(
     Currently, vLLM supports the basic multi-head attention mechanism and its variant with rotary positional embeddings.
     If your model employs a different attention mechanism, you will need to implement a new attention layer in vLLM.
 
-For reference, check out our [Llama implementation](../../../vllm/model_executor/models/llama.py). vLLM already supports a large number of models. It is recommended to find a model similar to yours and adapt it to your model's architecture. Check out [vllm/model_executor/models](../../../vllm/model_executor/models) for more examples.
+For reference, check out our [Llama implementation](gh-file:vllm/model_executor/models/llama.py). vLLM already supports a large number of models. It is recommended to find a model similar to yours and adapt it to your model's architecture. Check out <gh-dir:vllm/model_executor/models> for more examples.
 
 ## 3. (Optional) Implement tensor parallelism and quantization support
 
@@ -130,22 +130,22 @@ We consider 3 different scenarios:
 2. Models that combine Mamba layers (either Mamba-1 or Mamba-2) together with attention layers.
 3. Models that combine Mamba-like mechanisms (e.g., Linear Attention, ShortConv) together with attention layers.
 
-For case (1), we recommend looking at the implementation of [`MambaForCausalLM`](../../../vllm/model_executor/models/mamba.py) (for Mamba-1) or [`Mamba2ForCausalLM`](../../../vllm/model_executor/models/mamba2.py) (for Mamba-2) as a reference.
+For case (1), we recommend looking at the implementation of [`MambaForCausalLM`](gh-file:vllm/model_executor/models/mamba.py) (for Mamba-1) or [`Mamba2ForCausalLM`](gh-file:vllm/model_executor/models/mamba2.py) (for Mamba-2) as a reference.
 The model should inherit protocol `IsAttentionFree` and also implement class methods `get_mamba_state_dtype_from_config` and `get_mamba_state_shape_from_config` to calculate the state shapes and data types from the config.
-For the mamba layers themselves, please use the [`MambaMixer`](../../../vllm/model_executor/layers/mamba/mamba_mixer.py) (for Mamba-1) or [`MambaMixer2`](../../../vllm/model_executor/layers/mamba/mamba_mixer2.py) (for Mamba-2) classes.
+For the mamba layers themselves, please use the [`MambaMixer`](gh-file:vllm/model_executor/layers/mamba/mamba_mixer.py) (for Mamba-1) or [`MambaMixer2`](gh-file:vllm/model_executor/layers/mamba/mamba_mixer2.py) (for Mamba-2) classes.
 Please *do not* use the `MambaCacheManager` (deprecated in V1) or replicate any of the V0-specific code paths in the existing model implementations.
 V0-only classes and code will be removed in the very near future.
-The model should also be added to the `MODELS_CONFIG_MAP` dictionary in [vllm/model_executor/models/config.py](../../../vllm/model_executor/models/config.py) to ensure that the runtime defaults are optimized.
+The model should also be added to the `MODELS_CONFIG_MAP` dictionary in <gh-file:vllm/model_executor/models/config.py> to ensure that the runtime defaults are optimized.
 
-For case (2), we recommend using as a reference the implementation of [`JambaForCausalLM`](../../../vllm/model_executor/models/jamba.py) (for an example of a model that uses Mamba-1 and attention together) or [`BambaForCausalLM`](../../../vllm/model_executor/models/bamba.py) (for an example of a model that uses Mamba-2 and attention together).
+For case (2), we recommend using as a reference the implementation of [`JambaForCausalLM`](gh-file:vllm/model_executor/models/jamba.py) (for an example of a model that uses Mamba-1 and attention together) or [`BambaForCausalLM`](gh-file:vllm/model_executor/models/bamba.py) (for an example of a model that uses Mamba-2 and attention together).
 These models should follow the same instructions as case (1), but they should inherit protocol `IsHybrid` (instead of `IsAttentionFree`) and it is *not* necessary to add them to the `MODELS_CONFIG_MAP` (their runtime defaults will be inferred from the protocol).
 
-For case (3), we recommend looking at the implementation of [`MiniMaxText01ForCausalLM`](../../../vllm/model_executor/models/minimax_text_01.py) or [`Lfm2ForCausalLM`](../../../vllm/model_executor/models/lfm2.py) as a reference, which use custom "mamba-like" layers `MiniMaxText01LinearAttention` and `ShortConv` respectively.
+For case (3), we recommend looking at the implementation of [`MiniMaxText01ForCausalLM`](gh-file:vllm/model_executor/models/minimax_text_01.py) or [`Lfm2ForCausalLM`](gh-file:vllm/model_executor/models/lfm2.py) as a reference, which use custom "mamba-like" layers `MiniMaxText01LinearAttention` and `ShortConv` respectively.
 Please follow the same guidelines as case (2) for implementing these models.
 We use "mamba-like" to refer to layers that posses a state that is updated in-place, rather than being appended-to (like KV cache for attention).
 For implementing new custom mamba-like layers, one should inherit from `MambaBase` and implement the methods `get_state_dtype`, `get_state_shape` to calculate the data types and state shapes at runtime, as well as `mamba_type` and `get_attn_backend`.
 It is also necessary to implement the "attention meta-data" class which handles the meta-data that is common across all layers.
-Please see [`LinearAttentionMetadata`](../../../vllm/v1/attention/backends/linear_attn.py) or [`ShortConvAttentionMetadata`](../../../vllm/v1/attention/backends/short_conv_attn.py) for examples of this.
+Please see [`LinearAttentionMetadata`](gh-file:vllm/v1/attention/backends/linear_attn.py) or [`ShortConvAttentionMetadata`](gh-file:v1/attention/backends/short_conv_attn.py) for examples of this.
 Finally, if one wants to support torch compile and CUDA graphs, it necessary to wrap the call to the mamba-like layer inside a custom op and register it.
-Please see the calls to `direct_register_custom_op` in [vllm/model_executor/models/minimax_text_01.py](../../../vllm/model_executor/models/minimax_text_01.py) or [vllm/model_executor/layers/mamba/short_conv.py](../../../vllm/model_executor/layers/mamba/short_conv.py) for examples of this.
-The new custom op should then be added to the list `_attention_ops` in [vllm/config/compilation.py](../../../vllm/config/compilation.py) to ensure that piecewise CUDA graphs works as intended.
+Please see the calls to `direct_register_custom_op` in <gh-file:vllm/model_executor/models/minimax_text_01.py> or <gh-file:vllm/model_executor/layers/mamba/short_conv.py> for examples of this.
+The new custom op should then be added to the list `_attention_ops` in <gh-file:vllm/config/compilation.py> to ensure that piecewise CUDA graphs works as intended.

docs/contributing/model/multimodal.md

@@ -507,7 +507,7 @@ return a schema of the tensors outputted by the HF processor that are related to
     ```
 
     !!! note
-        Our [actual code](../../../vllm/model_executor/models/llava.py) additionally supports
+        Our [actual code](gh-file:vllm/model_executor/models/llava.py) additionally supports
         pre-computed image embeddings, which can be passed to be model via the `image_embeds` argument.
 
 === "With postprocessing: Fuyu"
@@ -569,7 +569,7 @@ return a schema of the tensors outputted by the HF processor that are related to
         ```
 
     !!! note
-        Our [actual code](../../../vllm/model_executor/models/fuyu.py) has special handling
+        Our [actual code](gh-file:vllm/model_executor/models/fuyu.py) has special handling
         for text-only inputs to prevent unnecessary warnings from HF processor.
 
     !!! note
@@ -828,8 +828,8 @@ Some HF processors directly insert feature tokens without replacing anything in
 
 Examples:
 
-- BLIP-2 (insert at start of prompt): [vllm/model_executor/models/blip2.py](../../../vllm/model_executor/models/blip2.py)
-- Molmo (insert after `<|endoftext|>` token): [vllm/model_executor/models/molmo.py](../../../vllm/model_executor/models/molmo.py)
+- BLIP-2 (insert at start of prompt): <gh-file:vllm/model_executor/models/blip2.py>
+- Molmo (insert after `<|endoftext|>` token): <gh-file:vllm/model_executor/models/molmo.py>
 
 ### Handling prompt updates unrelated to multi-modal data
 
@@ -837,9 +837,9 @@ Examples:
 
 Examples:
 
-- Chameleon (appends `sep_token`): [vllm/model_executor/models/chameleon.py](../../../vllm/model_executor/models/chameleon.py)
-- Fuyu (appends `boa_token`): [vllm/model_executor/models/fuyu.py](../../../vllm/model_executor/models/fuyu.py)
-- Molmo (applies chat template which is not defined elsewhere): [vllm/model_executor/models/molmo.py](../../../vllm/model_executor/models/molmo.py)
+- Chameleon (appends `sep_token`): <gh-file:vllm/model_executor/models/chameleon.py>
+- Fuyu (appends `boa_token`): <gh-file:vllm/model_executor/models/fuyu.py>
+- Molmo (applies chat template which is not defined elsewhere): <gh-file:vllm/model_executor/models/molmo.py>
 
 ### Custom HF processor
 
@@ -847,6 +847,6 @@ Some models don't define an HF processor class on HF Hub. In that case, you can
 
 Examples:
 
-- DeepSeek-VL2: [vllm/model_executor/models/deepseek_vl2.py](../../../vllm/model_executor/models/deepseek_vl2.py)
-- InternVL: [vllm/model_executor/models/internvl.py](../../../vllm/model_executor/models/internvl.py)
-- Qwen-VL: [vllm/model_executor/models/qwen_vl.py](../../../vllm/model_executor/models/qwen_vl.py)
+- DeepSeek-VL2: <gh-file:vllm/model_executor/models/deepseek_vl2.py>
+- InternVL: <gh-file:vllm/model_executor/models/internvl.py>
+- Qwen-VL: <gh-file:vllm/model_executor/models/qwen_vl.py>

docs/contributing/model/registration.md

@@ -8,11 +8,11 @@ This page provides detailed instructions on how to do so.
 
 ## Built-in models
 
-To add a model directly to the vLLM library, start by forking our [GitHub repository](https://github.com/vllm-project/vllm) and then [build it from source](../../getting_started/installation/gpu.md#build-wheel-from-source).
+To add a model directly to the vLLM library, start by forking our [GitHub repository](https://github.com/vllm-project/vllm) and then [build it from source][build-from-source].
 This gives you the ability to modify the codebase and test your model.
 
-After you have implemented your model (see [tutorial](basic.md)), put it into the [vllm/model_executor/models](../../../vllm/model_executor/models) directory.
-Then, add your model class to `_VLLM_MODELS` in [vllm/model_executor/models/registry.py](../../../vllm/model_executor/models/registry.py) so that it is automatically registered upon importing vLLM.
+After you have implemented your model (see [tutorial](basic.md)), put it into the <gh-dir:vllm/model_executor/models> directory.
+Then, add your model class to `_VLLM_MODELS` in <gh-file:vllm/model_executor/models/registry.py> so that it is automatically registered upon importing vLLM.
 Finally, update our [list of supported models](../../models/supported_models.md) to promote your model!
 
 !!! important

docs/contributing/model/tests.md

@@ -9,7 +9,7 @@ Without them, the CI for your PR will fail.
 
 ### Model loading
 
-Include an example HuggingFace repository for your model in [tests/models/registry.py](../../../tests/models/registry.py).
+Include an example HuggingFace repository for your model in <gh-file:tests/models/registry.py>.
 This enables a unit test that loads dummy weights to ensure that the model can be initialized in vLLM.
 
 !!! important
@@ -26,24 +26,26 @@ Passing these tests provides more confidence that your implementation is correct
 
 ### Model correctness
 
-These tests compare the model outputs of vLLM against [HF Transformers](https://github.com/huggingface/transformers). You can add new tests under the subdirectories of [tests/models](../../../tests/models).
+These tests compare the model outputs of vLLM against [HF Transformers](https://github.com/huggingface/transformers). You can add new tests under the subdirectories of <gh-dir:tests/models>.
 
 #### Generative models
 
-For [generative models](../../models/generative_models.md), there are two levels of correctness tests, as defined in [tests/models/utils.py](../../../tests/models/utils.py):
+For [generative models](../../models/generative_models.md), there are two levels of correctness tests, as defined in <gh-file:tests/models/utils.py>:
 
 - Exact correctness (`check_outputs_equal`): The text outputted by vLLM should exactly match the text outputted by HF.
 - Logprobs similarity (`check_logprobs_close`): The logprobs outputted by vLLM should be in the top-k logprobs outputted by HF, and vice versa.
 
 #### Pooling models
 
-For [pooling models](../../models/pooling_models.md), we simply check the cosine similarity, as defined in [tests/models/utils.py](../../../tests/models/utils.py).
+For [pooling models](../../models/pooling_models.md), we simply check the cosine similarity, as defined in <gh-file:tests/models/utils.py>.
+
+[](){ #mm-processing-tests }
 
 ### Multi-modal processing
 
 #### Common tests
 
-Adding your model to [tests/models/multimodal/processing/test_common.py](../../../tests/models/multimodal/processing/test_common.py) verifies that the following input combinations result in the same outputs:
+Adding your model to <gh-file:tests/models/multimodal/processing/test_common.py> verifies that the following input combinations result in the same outputs:
 
 - Text + multi-modal data
 - Tokens + multi-modal data
@@ -52,6 +54,6 @@ Adding your model to [tests/models/multimodal/processing/test_common.py](../../.
 
 #### Model-specific tests
 
-You can add a new file under [tests/models/multimodal/processing](../../../tests/models/multimodal/processing) to run tests that only apply to your model.
+You can add a new file under <gh-dir:tests/models/multimodal/processing> to run tests that only apply to your model.
 
-For example, if the HF processor for your model accepts user-specified keyword arguments, you can verify that the keyword arguments are being applied correctly, such as in [tests/models/multimodal/processing/test_phi3v.py](../../../tests/models/multimodal/processing/test_phi3v.py).
+For example, if the HF processor for your model accepts user-specified keyword arguments, you can verify that the keyword arguments are being applied correctly, such as in <gh-file:tests/models/multimodal/processing/test_phi3v.py>.

docs/contributing/model/transcription.md

@@ -248,9 +248,9 @@ No extra registration is required beyond having your model class available via t
 
 ## Examples in-tree
 
-- Whisper encoder–decoder (audio-only): [vllm/model_executor/models/whisper.py](../../../vllm/model_executor/models/whisper.py)
-- Voxtral decoder-only (audio embeddings + LLM): [vllm/model_executor/models/voxtral.py](../../../vllm/model_executor/models/voxtral.py)
-- Gemma3n decoder-only with fixed instruction prompt: [vllm/model_executor/models/gemma3n_mm.py](../../../vllm/model_executor/models/gemma3n_mm.py)
+- Whisper encoder–decoder (audio-only): <gh-file:vllm/model_executor/models/whisper.py>
+- Voxtral decoder-only (audio embeddings + LLM): <gh-file:vllm/model_executor/models/voxtral.py>
+- Gemma3n decoder-only with fixed instruction prompt: <gh-file:vllm/model_executor/models/gemma3n_mm.py>
 
 ## Test with the API
 
@@ -278,7 +278,7 @@ Once your model implements `SupportsTranscription`, you can test the endpoints (
       http://localhost:8000/v1/audio/translations
     ```
 
-Or check out more examples in [examples/online_serving](../../../examples/online_serving).
+Or check out more examples in <gh-file:examples/online_serving>.
 
 !!! note
     - If your model handles chunking internally (e.g., via its processor or encoder), set `min_energy_split_window_size=None` in the returned `SpeechToTextConfig` to disable server-side chunking.

docs/contributing/profiling.md

@@ -33,7 +33,7 @@ Traces can be visualized using <https://ui.perfetto.dev/>.
 
 #### Offline Inference
 
-Refer to [examples/offline_inference/simple_profiling.py](../../examples/offline_inference/simple_profiling.py) for an example.
+Refer to <gh-file:examples/offline_inference/simple_profiling.py> for an example.
 
 #### OpenAI Server
 
@@ -180,13 +180,9 @@ The profiling traces generated by the continuous profiling workflow are publicly
 The Python standard library includes
 [cProfile](https://docs.python.org/3/library/profile.html) for profiling Python
 code. vLLM includes a couple of helpers that make it easy to apply it to a section of vLLM.
-Both the `vllm.utils.profiling.cprofile` and `vllm.utils.profiling.cprofile_context` functions can be
+Both the `vllm.utils.cprofile` and `vllm.utils.cprofile_context` functions can be
 used to profile a section of code.
 
-!!! note
-    The legacy import paths `vllm.utils.cprofile` and `vllm.utils.cprofile_context` are deprecated.
-    Please use `vllm.utils.profiling.cprofile` and `vllm.utils.profiling.cprofile_context` instead.
-
 ### Example usage - decorator
 
 The first helper is a Python decorator that can be used to profile a function.
@@ -194,9 +190,9 @@ If a filename is specified, the profile will be saved to that file. If no filena
 specified, profile data will be printed to stdout.
 
 ```python
-from vllm.utils.profiling import cprofile
+import vllm.utils
 
-@cprofile("expensive_function.prof")
+@vllm.utils.cprofile("expensive_function.prof")
 def expensive_function():
     # some expensive code
     pass
@@ -208,13 +204,13 @@ The second helper is a context manager that can be used to profile a block of
 code. Similar to the decorator, the filename is optional.
 
 ```python
-from vllm.utils.profiling import cprofile_context
+import vllm.utils
 
 def another_function():
     # more expensive code
     pass
 
-with cprofile_context("another_function.prof"):
+with vllm.utils.cprofile_context("another_function.prof"):
     another_function()
 ```
 

docs/deployment/docker.md

@@ -1,5 +1,7 @@
 # Using Docker
 
+[](){ #deployment-docker-pre-built-image }
+
 ## Use vLLM's Official Docker Image
 
 vLLM offers an official Docker image for deployment.
@@ -8,7 +10,7 @@ The image can be used to run OpenAI compatible server and is available on Docker
 ```bash
 docker run --runtime nvidia --gpus all \
     -v ~/.cache/huggingface:/root/.cache/huggingface \
-    --env "HF_TOKEN=$HF_TOKEN" \
+    --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
     -p 8000:8000 \
     --ipc=host \
     vllm/vllm-openai:latest \
@@ -20,7 +22,7 @@ This image can also be used with other container engines such as [Podman](https:
 ```bash
 podman run --device nvidia.com/gpu=all \
   -v ~/.cache/huggingface:/root/.cache/huggingface \
-  --env "HF_TOKEN=$HF_TOKEN" \
+  --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
   -p 8000:8000 \
   --ipc=host \
   docker.io/vllm/vllm-openai:latest \
@@ -35,17 +37,17 @@ You can add any other [engine-args](../configuration/engine_args.md) you need af
     memory to share data between processes under the hood, particularly for tensor parallel inference.
 
 !!! note
-    Optional dependencies are not included in order to avoid licensing issues (e.g. <https://github.com/vllm-project/vllm/issues/8030>).
+    Optional dependencies are not included in order to avoid licensing issues (e.g. <gh-issue:8030>).
 
     If you need to use those dependencies (having accepted the license terms),
     create a custom Dockerfile on top of the base image with an extra layer that installs them:
 
     ```Dockerfile
-    FROM vllm/vllm-openai:v0.11.0
+    FROM vllm/vllm-openai:v0.9.0
 
     # e.g. install the `audio` optional dependencies
     # NOTE: Make sure the version of vLLM matches the base image!
-    RUN uv pip install --system vllm[audio]==0.11.0
+    RUN uv pip install --system vllm[audio]==0.9.0
     ```
 
 !!! tip
@@ -60,9 +62,11 @@ You can add any other [engine-args](../configuration/engine_args.md) you need af
     RUN uv pip install --system git+https://github.com/huggingface/transformers.git
     ```
 
+[](){ #deployment-docker-build-image-from-source }
+
 ## Building vLLM's Docker Image from Source
 
-You can build and run vLLM from source via the provided [docker/Dockerfile](../../docker/Dockerfile). To build vLLM:
+You can build and run vLLM from source via the provided <gh-file:docker/Dockerfile>. To build vLLM:
 
 ```bash
 # optionally specifies: --build-arg max_jobs=8 --build-arg nvcc_threads=2
@@ -124,7 +128,7 @@ To run vLLM with the custom-built Docker image:
 docker run --runtime nvidia --gpus all \
     -v ~/.cache/huggingface:/root/.cache/huggingface \
     -p 8000:8000 \
-    --env "HF_TOKEN=<secret>" \
+    --env "HUGGING_FACE_HUB_TOKEN=<secret>" \
     vllm/vllm-openai <args...>
 ```
 

docs/deployment/frameworks/anyscale.md

@@ -1,9 +1,11 @@
 # Anyscale
 
+[](){ #deployment-anyscale }
+
 [Anyscale](https://www.anyscale.com) is a managed, multi-cloud platform developed by the creators of Ray.
 
 Anyscale automates the entire lifecycle of Ray clusters in your AWS, GCP, or Azure account, delivering the flexibility of open-source Ray
-without the operational overhead of maintaining Kubernetes control planes, configuring autoscalers, managing observability stacks, or manually managing head and worker nodes with helper scripts like [examples/online_serving/run_cluster.sh](../../../examples/online_serving/run_cluster.sh).
+without the operational overhead of maintaining Kubernetes control planes, configuring autoscalers, managing observability stacks, or manually managing head and worker nodes with helper scripts like <gh-file:examples/online_serving/run_cluster.sh>.
 
 When serving large language models with vLLM, Anyscale can rapidly provision [production-ready HTTPS endpoints](https://docs.anyscale.com/examples/deploy-ray-serve-llms) or [fault-tolerant batch inference jobs](https://docs.anyscale.com/examples/ray-data-llm).
 

docs/deployment/frameworks/lws.md

@@ -35,7 +35,7 @@ Deploy the following yaml file `lws.yaml`
               - name: vllm-leader
                 image: docker.io/vllm/vllm-openai:latest
                 env:
-                  - name: HF_TOKEN
+                  - name: HUGGING_FACE_HUB_TOKEN
                     value: <your-hf-token>
                 command:
                   - sh
@@ -83,7 +83,7 @@ Deploy the following yaml file `lws.yaml`
                     ephemeral-storage: 800Gi
                     cpu: 125
                 env:
-                  - name: HF_TOKEN
+                  - name: HUGGING_FACE_HUB_TOKEN
                     value: <your-hf-token>
                 volumeMounts:
                   - mountPath: /dev/shm