[2/N][Refactor][Qwen3-Next] remove redundant methods and patch methods in Qwen3NextGatedDeltaNet (#3082)
### What this PR does / why we need it?
remove redundant methods and patch methods in Qwen3NextGatedDeltaNet
involved causal_conv1d_fn, causal_conv1d_update_npu, fused_gdn_gating,
fused_reccrrent_gated_delta_rule, torch_chunk_gated_delta_rule,
RMSNormGated
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
```
def main():
prompts = [
"The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(max_tokens=100, temperature=0.6, top_k=40, top_p=0.95)
# Create an LLM.
llm = LLM(
model="Qwen/Qwen3-Next-80B-A3B-Instruct",
tensor_parallel_size=4,
enforce_eager=True,
trust_remote_code=True,
max_model_len=256,
gpu_memory_utilization=0.7,
block_size=64,
)
# Generate texts from the prompts.
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
CI passed with new added/existing test.
- vLLM version: v0.10.2
- vLLM main:
https://github.com/vllm-project/vllm/commit/5aeb9254521023f97aca292b3478aa7ff485ffb2
---------
Signed-off-by: Icey <1790571317@qq.com>
2025-09-24 11:25:42 +08:00
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import vllm.model_executor.layers.mamba.ops.causal_conv1d
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2025-11-28 20:55:43 +08:00
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from vllm_ascend.ops.triton.fla.chunk import chunk_gated_delta_rule
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from vllm_ascend.ops.triton.fla.layernorm_guard import LayerNormFn
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2025-11-26 11:06:12 +08:00
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from vllm_ascend.ops.triton.fla.sigmoid_gating import \
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2025-11-24 17:08:20 +08:00
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fused_recurrent_gated_delta_rule_fwd_kernel
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2025-12-11 15:52:39 +08:00
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from vllm_ascend.ops.triton.mamba.causal_conv1d import (
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2025-11-26 11:06:12 +08:00
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causal_conv1d_fn, causal_conv1d_update_npu)
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[2/N][Refactor][Qwen3-Next] remove redundant methods and patch methods in Qwen3NextGatedDeltaNet (#3082)
### What this PR does / why we need it?
remove redundant methods and patch methods in Qwen3NextGatedDeltaNet
involved causal_conv1d_fn, causal_conv1d_update_npu, fused_gdn_gating,
fused_reccrrent_gated_delta_rule, torch_chunk_gated_delta_rule,
RMSNormGated
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
```
def main():
prompts = [
"The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(max_tokens=100, temperature=0.6, top_k=40, top_p=0.95)
# Create an LLM.
llm = LLM(
model="Qwen/Qwen3-Next-80B-A3B-Instruct",
tensor_parallel_size=4,
enforce_eager=True,
trust_remote_code=True,
max_model_len=256,
gpu_memory_utilization=0.7,
block_size=64,
)
# Generate texts from the prompts.
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
CI passed with new added/existing test.
- vLLM version: v0.10.2
- vLLM main:
https://github.com/vllm-project/vllm/commit/5aeb9254521023f97aca292b3478aa7ff485ffb2
---------
Signed-off-by: Icey <1790571317@qq.com>
2025-09-24 11:25:42 +08:00
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vllm.model_executor.layers.mamba.ops.causal_conv1d.causal_conv1d_update = causal_conv1d_update_npu
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vllm.model_executor.layers.mamba.ops.causal_conv1d.causal_conv1d_fn = causal_conv1d_fn
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2025-11-24 17:08:20 +08:00
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vllm.model_executor.layers.fla.ops.fused_recurrent.fused_recurrent_gated_delta_rule_fwd_kernel = fused_recurrent_gated_delta_rule_fwd_kernel
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[2/N][Refactor][Qwen3-Next] remove redundant methods and patch methods in Qwen3NextGatedDeltaNet (#3082)
### What this PR does / why we need it?
remove redundant methods and patch methods in Qwen3NextGatedDeltaNet
involved causal_conv1d_fn, causal_conv1d_update_npu, fused_gdn_gating,
fused_reccrrent_gated_delta_rule, torch_chunk_gated_delta_rule,
RMSNormGated
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
```
def main():
prompts = [
"The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(max_tokens=100, temperature=0.6, top_k=40, top_p=0.95)
# Create an LLM.
llm = LLM(
model="Qwen/Qwen3-Next-80B-A3B-Instruct",
tensor_parallel_size=4,
enforce_eager=True,
trust_remote_code=True,
max_model_len=256,
gpu_memory_utilization=0.7,
block_size=64,
)
# Generate texts from the prompts.
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
CI passed with new added/existing test.
- vLLM version: v0.10.2
- vLLM main:
https://github.com/vllm-project/vllm/commit/5aeb9254521023f97aca292b3478aa7ff485ffb2
---------
Signed-off-by: Icey <1790571317@qq.com>
2025-09-24 11:25:42 +08:00
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vllm.model_executor.layers.fla.ops.layernorm_guard.LayerNormFn = LayerNormFn
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2025-12-03 14:17:37 +08:00
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vllm.model_executor.layers.fla.ops.chunk_gated_delta_rule = chunk_gated_delta_rule
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