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Simplify the Config code (#2770)

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* simplify the code

* fix vl

* delete config

* fix

* perfect code

* fix ci

* fix xpu

* fix xpu

* fix server

* resolve conflict

* fix mtp

* resolve conflict

* fix xpu

* fix xpu

* fix vl

* fix log

* fix qwen moe

* fix qwen moe

* fix qwen moe
This commit is contained in:
YuanRisheng
2025-07-14 19:50:05 +08:00
committed by GitHub
parent 2e81792d64
commit 4c7b8bc458
34 changed files with 551 additions and 911 deletions
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28
fastdeploy/worker/gpu_model_runner.py
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@@ -259,7 +259,7 @@ class GPUModelRunner(ModelRunnerBase):
self.share_inputs["min_dec_len"][idx:idx + 1] = request.get(
"min_tokens", 1)
self.share_inputs["max_dec_len"][idx:idx + 1] = request.get(
"max_tokens", self.model_config.max_length)
"max_tokens", self.model_config.max_model_len)
self.share_inputs["stop_flags"][idx:idx + 1] = False
self.share_inputs["first_token_ids"][
@@ -375,11 +375,11 @@ class GPUModelRunner(ModelRunnerBase):
self.share_inputs["min_dec_len"] = paddle.full(
[max_num_seqs, 1], self.model_config.min_length, dtype='int64')
self.share_inputs["max_dec_len"] = paddle.full(
[max_num_seqs, 1], self.model_config.max_length, dtype='int64')
[max_num_seqs, 1], self.model_config.max_model_len, dtype='int64')
self.share_inputs["min_length"] = paddle.full(
[max_num_seqs, 1], self.model_config.min_length, dtype='int64')
self.share_inputs["max_length"] = paddle.full(
[max_num_seqs, 1], self.model_config.max_length, dtype='int64')
[max_num_seqs, 1], self.model_config.max_model_len, dtype='int64')
self.share_inputs["seq_lens_this_time"] = paddle.full(max_num_seqs,
0,
dtype='int32')
@@ -666,13 +666,13 @@ class GPUModelRunner(ModelRunnerBase):
# Get kv cache shape
kv_cache_shape = self.attn_backends[0].get_kv_cache_shape(
max_num_blocks=max_block_num)
local_rank = self.local_rank % self.parallel_config.tensor_parallel_degree
local_rank = self.local_rank % self.parallel_config.tensor_parallel_size
if not self.parallel_config.do_profile and (
self.parallel_config.enable_prefix_caching \
or self.parallel_config.splitwise_role != "mixed"):
cache_kvs_list = []
for i in range(self.model_config.num_layers):
for i in range(self.model_config.num_hidden_layers):
key_cache = paddle.empty(shape=[], dtype=cache_type)
key_cache_name = f"key_caches_{i}_rank{local_rank}.device{self.device_id}"
val_cache_name = f"value_caches_{i}_rank{local_rank}.device{self.device_id}"
@@ -687,7 +687,7 @@ class GPUModelRunner(ModelRunnerBase):
self.share_inputs["caches"] = cache_kvs_list
else:
for i in range(self.model_config.num_layers):
for i in range(self.model_config.num_hidden_layers):
cache_kvs["key_caches_{}".format(i)] = paddle.full(
shape=kv_cache_shape,
@@ -710,10 +710,10 @@ class GPUModelRunner(ModelRunnerBase):
"""
assert len(self.attn_backends) == 0
num_heads = self.model_config.num_attention_heads // self.parallel_config.tensor_parallel_degree
num_heads = self.model_config.num_attention_heads // self.parallel_config.tensor_parallel_size
self.model_config.kv_num_heads = max(1, int(
self.model_config.num_key_value_heads
) // self.parallel_config.tensor_parallel_degree)
) // self.parallel_config.tensor_parallel_size)
head_dim = self.model_config.head_dim
# Get the attention backend
@@ -787,14 +787,14 @@ class GPUModelRunner(ModelRunnerBase):
)
sampler_output = self.sampler(logits,
self.sampling_metadata)
if self.parallel_config.tensor_parallel_degree > 1:
if self.parallel_config.tensor_parallel_size > 1:
paddle.distributed.broadcast(sampler_output.sampled_token_ids, 0)
else:
self.sampler(logits, self.sampling_metadata,
self.parallel_config.max_model_len,
self.share_inputs)
sampler_output = None
if self.parallel_config.tensor_parallel_degree > 1:
if self.parallel_config.tensor_parallel_size > 1:
paddle.distributed.broadcast(
self.share_inputs["accept_tokens"], 0)
paddle.distributed.broadcast(
@@ -1021,14 +1021,14 @@ class GPUModelRunner(ModelRunnerBase):
self.sampling_metadata,
skip_idx_list,
)
if self.parallel_config.tensor_parallel_degree > 1:
if self.parallel_config.tensor_parallel_size > 1:
paddle.distributed.broadcast(sampler_output.sampled_token_ids, 0)
else:
self.sampler(logits, self.sampling_metadata,
self.parallel_config.max_model_len, self.share_inputs)
sampler_output = None
if self.parallel_config.tensor_parallel_degree > 1:
if self.parallel_config.tensor_parallel_size > 1:
paddle.distributed.broadcast(
self.share_inputs["accept_tokens"], 0)
paddle.distributed.broadcast(self.share_inputs["accept_num"],
@@ -1206,11 +1206,11 @@ class GPUModelRunner(ModelRunnerBase):
hidden_dim = self.model_config.head_dim * self.model_config.kv_num_heads
# NOTE(liuzichang): Implement multi-layer MTP architecture in the future
num_layers = self.model_config.num_layers + \
num_layers = self.model_config.num_hidden_layers + \
self.speculative_config.num_gpu_block_expand_ratio if \
self.speculative_method in [
"mtp"
] else self.model_config.num_layers
] else self.model_config.num_hidden_layers
required_memory = (
byte_of_dtype * 2 * # k + v
(self.parallel_config.block_size * hidden_dim) * num_layers)