mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-04 09:32:00 +00:00
9b596417af
* 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
60 lines
2.1 KiB
Python
Executable File
60 lines
2.1 KiB
Python
Executable File
#!/usr/bin/env python3
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from glob import glob
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import os
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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"]
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SOURCE_FATTN_VEC = """// This file has been autogenerated by generate_cu_files.py, do not edit manually.
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#include "../fattn-vec-f{vkq_size}.cuh"
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DECL_FATTN_VEC_F{vkq_size}_CASE({head_size}, {type_k}, {type_v});
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"""
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SOURCE_FATTN_WMMA_START = """// This file has been autogenerated by generate_cu_files.py, do not edit manually.
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#include "../fattn-wmma-f16.cuh"
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"""
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SOURCE_FATTN_WMMA_CASE = "DECL_FATTN_WMMA_F16_CASE({head_size}, {cols_per_block}, {kq_acc_t});\n"
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def get_short_name(long_quant_name):
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return long_quant_name.replace("GGML_TYPE_", "").lower()
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def get_head_sizes(type_k, type_v):
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if type_k == "GGML_TYPE_F16" and type_v == "GGML_TYPE_F16":
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return [64, 128, 256]
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if type_k == "GGML_TYPE_F16":
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return [64, 128]
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return [128]
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for filename in glob("*.cu"):
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os.remove(filename)
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for vkq_size in [16, 32]:
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for type_k in TYPES_KV:
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for type_v in TYPES_KV:
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for head_size in get_head_sizes(type_k, type_v):
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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:
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f.write(SOURCE_FATTN_VEC.format(vkq_size=vkq_size, head_size=head_size, type_k=type_k, type_v=type_v))
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for kq_acc_t in ["half", "float"]:
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for cols_per_block in [8, 16, 32]:
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if kq_acc_t == "float" and cols_per_block == 8:
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continue
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with open(f"fattn-wmma-f16-instance-kq{kq_acc_t}-cpb{cols_per_block}.cu", "w") as f:
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f.write(SOURCE_FATTN_WMMA_START)
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for head_size in [64, 80, 96, 112, 128, 256]:
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if cols_per_block == 8 and head_size % 32 != 0: # wmma fragment is 8x32
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continue
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if kq_acc_t == "float" and cols_per_block == 32 and head_size == 256: # register spilling, bad performance
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continue
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f.write(SOURCE_FATTN_WMMA_CASE.format(kq_acc_t=kq_acc_t, cols_per_block=cols_per_block, head_size=head_size))
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