CUDA: quantized KV support for FA vec (#7527)

* CUDA: quantized KV support for FA vec

* try CI fix

* fix commented-out kernel variants

* add q8_0 q4_0 tests

* fix nwarps > batch size

* split fattn compile via extern templates

* fix flake8

* fix metal tests

* fix cmake

* make generate_cu_files.py executable

* add autogenerated .cu files

* fix AMD

* error if type_v != FP16 and not flash_attn

* remove obsolete code

ggml-cuda/template-instances/generate_cu_files.py

@@ -0,0 +1,59 @@
+#!/usr/bin/env python3
+
+from glob import glob
+import os
+
+TYPES_KV = ["GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0", "GGML_TYPE_F16"]
+
+SOURCE_FATTN_VEC = """// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f{vkq_size}.cuh"
+
+DECL_FATTN_VEC_F{vkq_size}_CASE({head_size}, {type_k}, {type_v});
+"""
+
+SOURCE_FATTN_WMMA_START = """// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-wmma-f16.cuh"
+
+"""
+
+SOURCE_FATTN_WMMA_CASE = "DECL_FATTN_WMMA_F16_CASE({head_size}, {cols_per_block}, {kq_acc_t});\n"
+
+
+def get_short_name(long_quant_name):
+    return long_quant_name.replace("GGML_TYPE_", "").lower()
+
+
+def get_head_sizes(type_k, type_v):
+    if type_k == "GGML_TYPE_F16" and type_v == "GGML_TYPE_F16":
+        return [64, 128, 256]
+    if type_k == "GGML_TYPE_F16":
+        return [64, 128]
+    return [128]
+
+
+for filename in glob("*.cu"):
+    os.remove(filename)
+
+for vkq_size in [16, 32]:
+    for type_k in TYPES_KV:
+        for type_v in TYPES_KV:
+            for head_size in get_head_sizes(type_k, type_v):
+                with open(f"fattn-vec-f{vkq_size}-instance-hs{head_size}-{get_short_name(type_k)}-{get_short_name(type_v)}.cu", "w") as f:
+                    f.write(SOURCE_FATTN_VEC.format(vkq_size=vkq_size, head_size=head_size, type_k=type_k, type_v=type_v))
+
+for kq_acc_t in ["half", "float"]:
+    for cols_per_block in [8, 16, 32]:
+        if kq_acc_t == "float" and cols_per_block == 8:
+            continue
+
+        with open(f"fattn-wmma-f16-instance-kq{kq_acc_t}-cpb{cols_per_block}.cu", "w") as f:
+            f.write(SOURCE_FATTN_WMMA_START)
+
+            for head_size in [64, 80, 96, 112, 128, 256]:
+                if cols_per_block == 8 and head_size % 32 != 0: # wmma fragment is 8x32
+                    continue
+                if kq_acc_t == "float" and cols_per_block == 32 and head_size == 256: # register spilling, bad performance
+                    continue
+                f.write(SOURCE_FATTN_WMMA_CASE.format(kq_acc_t=kq_acc_t, cols_per_block=cols_per_block, head_size=head_size))