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Commit b0b9d2d9 authored by zhuwenwen's avatar zhuwenwen
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Merge branch 'v0.5.0-dtk24.04.1' into v0.5.3.post1-dtk24.04.1

parents c9305344 ffbef65c
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README.md
View file @ b0b9d2d9
......@@ -54,7 +54,10 @@ pip install setuptools wheel
```shell
git clone http://developer.hpccube.com/codes/OpenDAS/vllm.git # 根据需要的分支进行切换
```
安装依赖：
```shell
pip install -r requirements-rocm.txt
```
- 提供2种源码编译方式（进入vllm目录）：
```
1. 编译whl包并安装
......
benchmarks/benchmark_throughput.py
View file @ b0b9d2d9
......@@ -5,12 +5,14 @@ import random
import time
from typing import List, Optional, Tuple
import numpy as np
import torch
from tqdm import tqdm
from transformers import (AutoModelForCausalLM, AutoTokenizer,
PreTrainedTokenizerBase)
from vllm.engine.arg_utils import EngineArgs
from vllm.inputs import PromptInputs
from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
from vllm.utils import FlexibleArgumentParser
......@@ -123,6 +125,23 @@ def run_vllm(
max_tokens=output_len,
))
# warmup
dummy_prompt_token_ids = np.random.randint(10000,
size=(args.batch_size,
args.input_len))
dummy_inputs: List[PromptInputs] = [{
"prompt_token_ids": batch
} for batch in dummy_prompt_token_ids.tolist()]
def run_to_completion():
llm.generate(dummy_inputs,
sampling_params=sampling_params,
use_tqdm=False)
print("Warming up...")
for _ in tqdm(range(args.num_iters_warmup), desc="Warmup iterations"):
run_to_completion()
start = time.perf_counter()
llm.generate(prompts, sampling_params, use_tqdm=True)
end = time.perf_counter()
......@@ -299,6 +318,10 @@ if __name__ == "__main__":
default=1,
help="Number of generated sequences per prompt.")
parser.add_argument("--use-beam-search", action="store_true")
parser.add_argument('--num-iters-warmup',
type=int,
default=1,
help='Number of iterations to run for warmup.')
parser.add_argument("--num-prompts",
type=int,
default=1000,
......
csrc/attention/static_switch.h
View file @ b0b9d2d9
......@@ -31,38 +31,28 @@
} \
}()
// #define HEADSIZE_SWITCH(HEADDIM, ...) \
// [&] { \
// if (HEADDIM == 64) { \
// constexpr static int HEAD_SIZE = 64; \
// return __VA_ARGS__(); \
// } else if (HEADDIM == 80) { \
// constexpr static int HEAD_SIZE = 80; \
// return __VA_ARGS__(); \
// } else if (HEADDIM == 96) { \
// constexpr static int HEAD_SIZE = 96; \
// return __VA_ARGS__(); \
// } else if (HEADDIM == 112) { \
// constexpr static int HEAD_SIZE = 112; \
// return __VA_ARGS__(); \
// } else if (HEADDIM == 128) { \
// constexpr static int HEAD_SIZE = 128; \
// return __VA_ARGS__(); \
// } else if (HEADDIM == 256) { \
// constexpr static int HEAD_SIZE = 256; \
// return __VA_ARGS__(); \
// } \
// else { \
// TORCH_CHECK(false, "Unsupported head size: ", HEADDIM);\
// } \
// }()
#define HEADSIZE_SWITCH(HEADDIM, ...) \
[&] { \
if (HEADDIM == 128) { \
if (HEADDIM == 64) { \
constexpr static int HEAD_SIZE = 64; \
return __VA_ARGS__(); \
} else if (HEADDIM == 80) { \
constexpr static int HEAD_SIZE = 80; \
return __VA_ARGS__(); \
} else if (HEADDIM == 96) { \
constexpr static int HEAD_SIZE = 96; \
return __VA_ARGS__(); \
} else if (HEADDIM == 112) { \
constexpr static int HEAD_SIZE = 112; \
return __VA_ARGS__(); \
} else if (HEADDIM == 128) { \
constexpr static int HEAD_SIZE = 128; \
return __VA_ARGS__(); \
} else { \
} else if (HEADDIM == 256) { \
constexpr static int HEAD_SIZE = 256; \
return __VA_ARGS__(); \
} \
else { \
TORCH_CHECK(false, "Unsupported head size: ", HEADDIM);\
} \
}()
......
examples/offline_inference.py
View file @ b0b9d2d9
......@@ -12,7 +12,7 @@ if __name__ == '__main__':
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
# Create an LLM.
llm = LLM(model="facebook/opt-125m",trust_remote_code=True, dtype="float16", enforce_eager=False)
llm = LLM(model="facebook/opt-125m",trust_remote_code=True, dtype="float16", enforce_eager=True)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
outputs = llm.generate(prompts, sampling_params)
......
setup.py
View file @ b0b9d2d9
......@@ -377,7 +377,7 @@ def get_version_add(sha: Optional[str] = None) -> str:
if sha != 'Unknown':
if sha is None:
sha = get_sha(vllm_root)
version = 'das1.1.git' + sha[:7]
version = 'das1.2.git' + sha[:7]
# abi version
version += "." + get_abi()
......
vllm/model_executor/models/baichuan.py
View file @ b0b9d2d9
......@@ -186,7 +186,7 @@ class BaiChuanAttention(nn.Module):
attn_metadata: AttentionMetadata,
) -> torch.Tensor:
qkv, _ = self.W_pack(hidden_states)
if os.environ.get('FA_PAD') == '1' and qkv.shape[-1] == 12320:
if os.environ.get('FA_PAD') == '1':
qkv = qkv[...,:-32]
q, k, v = qkv.chunk(chunks=3, dim=-1)
if self.postion_embedding != "ALIBI":
......@@ -423,13 +423,17 @@ class BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA):
]
combined_words = "|".join(lay_key_words)
lay_qkv_words = ["self_attn.W_pack.weight"]
qkv_words = "|".join(lay_qkv_words)
for layername, weight in params_dict.items():
matches = re.findall(combined_words, layername)
if matches:
if self.use_gemm_pad and gemm_bank_conf(weight.data.shape[0]):
weight.data = pad_weight(weight.data, 32)
if self.use_fa_pad and weight.data.shape[0] == 12288:
if self.use_fa_pad and (re.findall(qkv_words, layername)):
if not gemm_bank_conf(weight.data.shape[0]):
weight.data = pad_weight(weight.data, 32)
_weight = torch.zeros_like(weight.data)
......
vllm/model_executor/models/chatglm.py
View file @ b0b9d2d9
......@@ -108,7 +108,7 @@ class GLMAttention(nn.Module):
attn_metadata: AttentionMetadata,
) -> torch.Tensor:
qkv, _ = self.query_key_value(hidden_states)
if os.environ.get('FA_PAD') == '1' and qkv.shape[-1] == 12320:
if os.environ.get('FA_PAD') == '1':
qkv = qkv[...,:-32]
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
q, k = self.rotary_emb(position_ids, q, k)
......@@ -421,13 +421,23 @@ class ChatGLMForCausalLM(nn.Module, SupportsLoRA):
]
combined_words = "|".join(lay_key_words)
lay_qkv_words = ["self_attention.query_key_value.weight"]
qkv_words = "|".join(lay_qkv_words)
lay_qkv_bias_words = ["self_attention.query_key_value.bias"]
qkv_bias_words = "|".join(lay_qkv_bias_words)
for layername, weight in params_dict.items():
if self.use_fa_pad and (re.findall(qkv_bias_words, layername)):
weight.data = pad_weight(weight.data, 32)
matches = re.findall(combined_words, layername)
if matches:
if self.use_gemm_pad and gemm_bank_conf(weight.data.shape[0]):
weight.data = pad_weight(weight.data, 32)
if self.use_fa_pad and weight.data.shape[0] == 12288:
if self.use_fa_pad and (re.findall(qkv_words, layername)):
if not gemm_bank_conf(weight.data.shape[0]):
weight.data = pad_weight(weight.data, 32)
_weight = torch.zeros_like(weight.data)
......
vllm/model_executor/models/llama.py
View file @ b0b9d2d9
......@@ -175,7 +175,7 @@ class LlamaAttention(nn.Module):
attn_metadata: AttentionMetadata,
) -> torch.Tensor:
qkv, _ = self.qkv_proj(hidden_states)
if os.environ.get('FA_PAD') == '1' and qkv.shape[-1] == 12320:
if os.environ.get('FA_PAD') == '1':
qkv = qkv[...,:-32]
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
q, k = self.rotary_emb(positions, q, k)
......@@ -531,13 +531,17 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA):
]
combined_words = "|".join(lay_key_words)
lay_qkv_words = ["self_attn.qkv_proj.weight"]
qkv_words = "|".join(lay_qkv_words)
for layername, weight in params_dict.items():
matches = re.findall(combined_words, layername)
if matches:
if self.use_gemm_pad and gemm_bank_conf(weight.data.shape[0]):
weight.data = pad_weight(weight.data, 32)
if self.use_fa_pad and weight.data.shape[0] == 12288:
if self.use_fa_pad and (re.findall(qkv_words, layername)):
if not gemm_bank_conf(weight.data.shape[0]):
weight.data = pad_weight(weight.data, 32)
_weight = torch.zeros_like(weight.data)
......
vllm/model_executor/models/qwen.py
View file @ b0b9d2d9
......@@ -124,7 +124,7 @@ class QWenAttention(nn.Module):
attn_metadata: AttentionMetadata,
) -> torch.Tensor:
qkv, _ = self.c_attn(hidden_states)
if os.environ.get('FA_PAD') == '1' and qkv.shape[-1] == 12320:
if os.environ.get('FA_PAD') == '1':
qkv = qkv[...,:-32]
q, k, v = qkv.chunk(chunks=3, dim=-1)
q, k = self.rotary_emb(positions, q, k)
......@@ -326,13 +326,23 @@ class QWenLMHeadModel(nn.Module):
]
combined_words = "|".join(lay_key_words)
lay_qkv_words = ["attn.c_attn.weight"]
qkv_words = "|".join(lay_qkv_words)
lay_qkv_bias_words = ["attn.c_attn.bias"]
qkv_bias_words = "|".join(lay_qkv_bias_words)
for layername, weight in params_dict.items():
if self.use_fa_pad and (re.findall(qkv_bias_words, layername)):
weight.data = pad_weight(weight.data, 32)
matches = re.findall(combined_words, layername)
if matches:
if self.use_gemm_pad and gemm_bank_conf(weight.data.shape[0]):
weight.data = pad_weight(weight.data, 32)
if self.use_fa_pad and weight.data.shape[0] == 12288:
if self.use_fa_pad and (re.findall(qkv_words, layername)):
if not gemm_bank_conf(weight.data.shape[0]):
weight.data = pad_weight(weight.data, 32)
_weight = torch.zeros_like(weight.data)
......
vllm/model_executor/models/qwen2.py
View file @ b0b9d2d9
......@@ -156,7 +156,7 @@ class Qwen2Attention(nn.Module):
attn_metadata: AttentionMetadata,
) -> torch.Tensor:
qkv, _ = self.qkv_proj(hidden_states)
if os.environ.get('FA_PAD') == '1' and qkv.shape[-1] == 12320:
if os.environ.get('FA_PAD') == '1':
qkv = qkv[...,:-32]
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
q, k = self.rotary_emb(positions, q, k)
......@@ -411,13 +411,23 @@ class Qwen2ForCausalLM(nn.Module, SupportsLoRA):
]
combined_words = "|".join(lay_key_words)
lay_qkv_words = ["self_attn.qkv_proj.weight"]
qkv_words = "|".join(lay_qkv_words)
lay_qkv_bias_words = ["self_attn.qkv_proj.bias"]
qkv_bias_words = "|".join(lay_qkv_bias_words)
for layername, weight in params_dict.items():
if self.use_fa_pad and (re.findall(qkv_bias_words, layername)):
weight.data = pad_weight(weight.data, 32)
matches = re.findall(combined_words, layername)
if matches:
if self.use_gemm_pad and gemm_bank_conf(weight.data.shape[0]):
weight.data = pad_weight(weight.data, 32)
if self.use_fa_pad and weight.data.shape[0] == 12288:
if self.use_fa_pad and (re.findall(qkv_words, layername)):
if not gemm_bank_conf(weight.data.shape[0]):
weight.data = pad_weight(weight.data, 32)
_weight = torch.zeros_like(weight.data)
......
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