[Bugs] Fix DeepGEMM pre-compile tools. (#3351)

Fix some miss cache problems.
Add README.md.

tools/deep_gemm_pre-compile/generate_config.py

@@ -17,7 +17,7 @@ import json
 import logging
 import math
 import os
-from typing import Tuple
+from typing import List, Tuple
 
 from fastdeploy.model_executor.ops.gpu.deep_gemm.jit_kernels.gemm import get_smem_config
 
@@ -27,33 +27,84 @@ logger.addHandler(console_handler)
 logger.setLevel(os.getenv("PRE_COMPILE_LOG_LEVEL", "INFO"))
 
 
-def generate_kn_pairs(model_cfg: dict) -> Tuple[list, list, list]:
+def generate_kn_pairs(args, model_cfg: dict) -> Tuple[List, List, List]:
     hidden_size = model_cfg["hidden_size"]
     intermediate_size = model_cfg["intermediate_size"]
     moe_intermediate_size = model_cfg["moe_intermediate_size"]
     num_attention_heads = model_cfg["num_attention_heads"]
     num_key_value_heads = model_cfg["num_key_value_heads"]
     head_dim = int(hidden_size / num_attention_heads)
-    gemm_kn_pairs = [
+    tp_size = args.tensor_parallel_size
+    ep_size = args.expert_parallel_size
+    has_shared_experts = args.has_shared_experts.lower() == "true"
+
+    gemm_kn_pairs = []
+    grouped_gemm_contiguous_kn_pairs = []
+    grouped_gemm_masked_kn_pairs = []
+    if tp_size > 1 and ep_size == 1:
+        logger.debug("Generating kn pairs for tensor parallel.")
         # Dense normal gemm
-        [hidden_size, intermediate_size * 2],
-        [intermediate_size, hidden_size],
-        [hidden_size, hidden_size],
-        [
-            hidden_size,
-            (num_attention_heads + num_key_value_heads * 2) * head_dim,
-        ],
-    ]
-    grouped_gemm_contiguous_kn_pairs = [
+        gemm_kn_pairs.extend(
+            [
+                [int(intermediate_size / tp_size), hidden_size],
+                [hidden_size, int(head_dim * (num_attention_heads + num_key_value_heads * 2) / tp_size)],
+                [hidden_size, int(intermediate_size * 2 / tp_size)],
+                [int(hidden_size / tp_size), hidden_size],
+            ]
+        )
+
         # Moe grouped gemm contiguous
-        [hidden_size, moe_intermediate_size * 2],
-        [moe_intermediate_size, hidden_size],
-    ]
-    grouped_gemm_masked_kn_pairs = [
+        grouped_gemm_contiguous_kn_pairs.extend(
+            [
+                [int(moe_intermediate_size / tp_size), hidden_size],
+                [hidden_size, int(moe_intermediate_size * 2 / tp_size)],
+            ]
+        )
+        if has_shared_experts:
+            logger.debug("Generating kn pairs for models with shared experts.")
+            gemm_kn_pairs.extend(
+                [
+                    [hidden_size, int(moe_intermediate_size * 4 / tp_size)],
+                    [int(moe_intermediate_size * 2 / tp_size), hidden_size],
+                ]
+            )
+    elif tp_size == 1 and ep_size > 1:
+        logger.debug("Generating kn pairs for expert parallel.")
+        # Dense normal gemm
+        gemm_kn_pairs.extend(
+            [
+                [intermediate_size, hidden_size],
+                [hidden_size, int(head_dim * (num_attention_heads + num_key_value_heads * 2))],
+                [hidden_size, int(intermediate_size * 2)],
+                [hidden_size, hidden_size],
+            ]
+        )
+        # Moe grouped gemm contiguous
+        grouped_gemm_contiguous_kn_pairs.extend(
+            [
+                [moe_intermediate_size, hidden_size],
+                [hidden_size, int(moe_intermediate_size * 2)],
+            ]
+        )
         # Moe grouped gemm masked
-        [hidden_size, moe_intermediate_size * 2],
-        [moe_intermediate_size, hidden_size],
-    ]
+        grouped_gemm_masked_kn_pairs.extend(
+            [
+                [moe_intermediate_size, hidden_size],
+                [hidden_size, int(moe_intermediate_size * 2)],
+            ]
+        )
+        if has_shared_experts:
+            logger.debug("Generating kn pairs for models with shared experts.")
+            gemm_kn_pairs.extend(
+                [
+                    [hidden_size, int(moe_intermediate_size * 4)],
+                    [int(moe_intermediate_size * 2), hidden_size],
+                ]
+            )
+    elif tp_size > 1 and ep_size > 1:
+        raise ValueError("Not supported to enable EP and TP at the same time for now.")
+    else:
+        raise ValueError("Please check the tensor parallel size and expert parallel size.")
 
     return (
         gemm_kn_pairs,
@@ -78,7 +129,8 @@ def generate_json(
     counter = 0
     with open(output_path, "a+", encoding="utf-8") as f:
         for block_m in BLOCK_MS:
-            for block_n in BLOCK_NS:
+            # NOTES: the block sizes can not be too large, so at least one dim less than 128
+            for block_n in filter(lambda bn: block_m <= 128 or bn <= 128, BLOCK_NS):
                 if 128 % block_n != 0 and 128 // math.gcd(128, block_n) <= 4:
                     NUM_STAGES = [4, 3]
                 else:
@@ -110,33 +162,43 @@ def generate_json(
 def main(args):
     with open(os.path.join(args.model, "config.json"), "r") as f:
         model_cfg = json.load(f)
-
+    logger.debug(
+        f"TP Size: {args.tensor_parallel_size}, "
+        f"EP Size: {args.expert_parallel_size}, "
+        f"has shared experts: {args.has_shared_experts}"
+    )
+    logger.info(f"Configurations generated and saved to {args.output}")
     (
         gemm_kn_pairs,
         grouped_gemm_contiguous_kn_pairs,
         grouped_gemm_masked_kn_pairs,
-    ) = generate_kn_pairs(model_cfg)
-    num_gemm = generate_json(
-        gemm_kn_pairs,
-        model_cfg["moe_num_experts"],
-        args.output,
-    )
-    num_grouped_contiguous = generate_json(
-        grouped_gemm_contiguous_kn_pairs,
-        model_cfg["moe_num_experts"],
-        args.output,
-        is_grouped_contiguous=True,
-    )
-    num_grouped_masked = generate_json(
-        grouped_gemm_masked_kn_pairs,
-        model_cfg["moe_num_experts"],
-        args.output,
-        is_grouped_masked=True,
-    )
-    logger.info(f"Configurations generated and saved to {args.output}")
-    logger.info(f"Generated {num_gemm} gemm configuration.")
-    logger.info(f"Generated {num_grouped_contiguous} grouped_gemm_contiguous configuration.")
-    logger.info(f"Generated {num_grouped_masked} grouped_gemm_masked configuration.")
+    ) = generate_kn_pairs(args, model_cfg)
+    logger.debug(f"GEMM KN pairs: {gemm_kn_pairs}")
+    logger.debug(f"Grouped GEMM Contiguous KN pairs: {grouped_gemm_contiguous_kn_pairs}")
+    logger.debug(f"Grouped GEMM Masked KN pairs: {grouped_gemm_masked_kn_pairs}")
+    if len(gemm_kn_pairs) > 0:
+        num_gemm = generate_json(
+            gemm_kn_pairs,
+            model_cfg["moe_num_experts"],
+            args.output,
+        )
+        logger.info(f"Generated {num_gemm} gemm configuration.")
+    if len(grouped_gemm_contiguous_kn_pairs) > 0:
+        num_grouped_contiguous = generate_json(
+            grouped_gemm_contiguous_kn_pairs,
+            model_cfg["moe_num_experts"],
+            args.output,
+            is_grouped_contiguous=True,
+        )
+        logger.info(f"Generated {num_grouped_contiguous} grouped_gemm_contiguous configuration.")
+    if len(grouped_gemm_masked_kn_pairs) > 0:
+        num_grouped_masked = generate_json(
+            grouped_gemm_masked_kn_pairs,
+            model_cfg["moe_num_experts"],
+            args.output,
+            is_grouped_masked=True,
+        )
+        logger.info(f"Generated {num_grouped_masked} grouped_gemm_masked configuration.")
 
 
 if __name__ == "__main__":
@@ -146,6 +208,23 @@ if __name__ == "__main__":
         type=str,
         required=True,
     )
+    parser.add_argument(
+        "--tensor-parallel-size",
+        "--tp",
+        type=int,
+        default=1,
+    )
+    parser.add_argument(
+        "--expert-parallel-size",
+        "--ep",
+        type=int,
+        default=1,
+    )
+    parser.add_argument(
+        "--has-shared-experts",
+        type=str,
+        default="False",
+    )
     parser.add_argument(
         "--output",
         type=str,