mirror of
https://github.com/ROCm/ROCm.git
synced 2026-01-07 22:03:58 -05:00
Update vLLM doc for 10/6 release and bump rocm-docs-core to 1.26.0 (#5481)
* archive previous doc version * update model/docker data and doc templates * Update "Reproducing the Docker image" * fix: truncated commit hash doesn't work for some reason * bump rocm-docs-core to 1.26.0 * fix numbering fix * update docker tag * update .wordlist.txt
This commit is contained in:
peterjunpark
@@ -313,6 +313,7 @@ Mooncake
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Mpops
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Multicore
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Multithreaded
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MXFP
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MyEnvironment
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MyST
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NANOO
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@@ -713,6 +714,7 @@ githooks
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github
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globals
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gnupg
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gpu
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grayscale
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gx
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gzip
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@@ -767,6 +769,7 @@ invariants
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invocating
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ipo
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jax
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json
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kdb
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kfd
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kv
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@@ -980,6 +983,7 @@ toolset
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toolsets
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torchtitan
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torchvision
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tp
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tqdm
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tracebacks
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txt
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@@ -0,0 +1,188 @@
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dockers:
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- pull_tag: rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909
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docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.10.1_20250909/images/sha256-1113268572e26d59b205792047bea0e61e018e79aeadceba118b7bf23cb3715c
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components:
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ROCm: 6.4.1
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vLLM: 0.10.1 (0.10.1rc2.dev409+g0b6bf6691.rocm641)
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PyTorch: 2.7.0+gitf717b2a
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hipBLASLt: 0.15
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model_groups:
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- group: Meta Llama
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tag: llama
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models:
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- model: Llama 3.1 8B
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mad_tag: pyt_vllm_llama-3.1-8b
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model_repo: meta-llama/Llama-3.1-8B-Instruct
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url: https://huggingface.co/meta-llama/Llama-3.1-8B
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precision: float16
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config:
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tp: 1
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dtype: auto
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kv_cache_dtype: auto
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max_seq_len_to_capture: 131072
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max_num_batched_tokens: 131072
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max_model_len: 8192
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- model: Llama 3.1 70B
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mad_tag: pyt_vllm_llama-3.1-70b
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model_repo: meta-llama/Llama-3.1-70B-Instruct
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url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
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precision: float16
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config:
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tp: 8
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dtype: auto
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kv_cache_dtype: auto
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max_seq_len_to_capture: 131072
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max_num_batched_tokens: 131072
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max_model_len: 8192
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- model: Llama 3.1 405B
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mad_tag: pyt_vllm_llama-3.1-405b
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model_repo: meta-llama/Llama-3.1-405B-Instruct
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url: https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct
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precision: float16
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config:
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tp: 8
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dtype: auto
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kv_cache_dtype: auto
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max_seq_len_to_capture: 131072
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max_num_batched_tokens: 131072
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max_model_len: 8192
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- model: Llama 2 70B
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mad_tag: pyt_vllm_llama-2-70b
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model_repo: meta-llama/Llama-2-70b-chat-hf
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url: https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
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precision: float16
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config:
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tp: 8
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||||
dtype: auto
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||||
kv_cache_dtype: auto
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||||
max_seq_len_to_capture: 4096
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max_num_batched_tokens: 4096
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max_model_len: 4096
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- model: Llama 3.1 8B FP8
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mad_tag: pyt_vllm_llama-3.1-8b_fp8
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||||
model_repo: amd/Llama-3.1-8B-Instruct-FP8-KV
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||||
url: https://huggingface.co/amd/Llama-3.1-8B-Instruct-FP8-KV
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precision: float8
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||||
config:
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||||
tp: 1
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||||
dtype: auto
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||||
kv_cache_dtype: fp8
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||||
max_seq_len_to_capture: 131072
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max_num_batched_tokens: 131072
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max_model_len: 8192
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||||
- model: Llama 3.1 70B FP8
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||||
mad_tag: pyt_vllm_llama-3.1-70b_fp8
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||||
model_repo: amd/Llama-3.1-70B-Instruct-FP8-KV
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||||
url: https://huggingface.co/amd/Llama-3.1-70B-Instruct-FP8-KV
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||||
precision: float8
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||||
config:
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||||
tp: 8
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||||
dtype: auto
|
||||
kv_cache_dtype: fp8
|
||||
max_seq_len_to_capture: 131072
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||||
max_num_batched_tokens: 131072
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max_model_len: 8192
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||||
- model: Llama 3.1 405B FP8
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||||
mad_tag: pyt_vllm_llama-3.1-405b_fp8
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||||
model_repo: amd/Llama-3.1-405B-Instruct-FP8-KV
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||||
url: https://huggingface.co/amd/Llama-3.1-405B-Instruct-FP8-KV
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||||
precision: float8
|
||||
config:
|
||||
tp: 8
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||||
dtype: auto
|
||||
kv_cache_dtype: fp8
|
||||
max_seq_len_to_capture: 131072
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||||
max_num_batched_tokens: 131072
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||||
max_model_len: 8192
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||||
- group: Mistral AI
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||||
tag: mistral
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||||
models:
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||||
- model: Mixtral MoE 8x7B
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||||
mad_tag: pyt_vllm_mixtral-8x7b
|
||||
model_repo: mistralai/Mixtral-8x7B-Instruct-v0.1
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||||
url: https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1
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||||
precision: float16
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
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||||
kv_cache_dtype: auto
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||||
max_seq_len_to_capture: 32768
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||||
max_num_batched_tokens: 32768
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||||
max_model_len: 8192
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||||
- model: Mixtral MoE 8x22B
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||||
mad_tag: pyt_vllm_mixtral-8x22b
|
||||
model_repo: mistralai/Mixtral-8x22B-Instruct-v0.1
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||||
url: https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1
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||||
precision: float16
|
||||
config:
|
||||
tp: 8
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||||
dtype: auto
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||||
kv_cache_dtype: auto
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||||
max_seq_len_to_capture: 65536
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||||
max_num_batched_tokens: 65536
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max_model_len: 8192
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||||
- model: Mixtral MoE 8x7B FP8
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||||
mad_tag: pyt_vllm_mixtral-8x7b_fp8
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||||
model_repo: amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
|
||||
url: https://huggingface.co/amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
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||||
precision: float8
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
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||||
kv_cache_dtype: fp8
|
||||
max_seq_len_to_capture: 32768
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||||
max_num_batched_tokens: 32768
|
||||
max_model_len: 8192
|
||||
- model: Mixtral MoE 8x22B FP8
|
||||
mad_tag: pyt_vllm_mixtral-8x22b_fp8
|
||||
model_repo: amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
|
||||
url: https://huggingface.co/amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
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||||
precision: float8
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: fp8
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||||
max_seq_len_to_capture: 65536
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||||
max_num_batched_tokens: 65536
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||||
max_model_len: 8192
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||||
- group: Qwen
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||||
tag: qwen
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||||
models:
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||||
- model: QwQ-32B
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||||
mad_tag: pyt_vllm_qwq-32b
|
||||
model_repo: Qwen/QwQ-32B
|
||||
url: https://huggingface.co/Qwen/QwQ-32B
|
||||
precision: float16
|
||||
config:
|
||||
tp: 1
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_seq_len_to_capture: 131072
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||||
max_num_batched_tokens: 131072
|
||||
max_model_len: 8192
|
||||
- model: Qwen3 30B A3B
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||||
mad_tag: pyt_vllm_qwen3-30b-a3b
|
||||
model_repo: Qwen/Qwen3-30B-A3B
|
||||
url: https://huggingface.co/Qwen/Qwen3-30B-A3B
|
||||
precision: float16
|
||||
config:
|
||||
tp: 1
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_seq_len_to_capture: 32768
|
||||
max_num_batched_tokens: 32768
|
||||
max_model_len: 8192
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||||
- group: Microsoft Phi
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||||
tag: phi
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||||
models:
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||||
- model: Phi-4
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||||
mad_tag: pyt_vllm_phi-4
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||||
model_repo: microsoft/phi-4
|
||||
url: https://huggingface.co/microsoft/phi-4
|
||||
config:
|
||||
tp: 1
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_seq_len_to_capture: 16384
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||||
max_num_batched_tokens: 16384
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max_model_len: 8192
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@@ -1,188 +1,316 @@
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||||
dockers:
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||||
- pull_tag: rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909
|
||||
docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.10.1_20250909/images/sha256-1113268572e26d59b205792047bea0e61e018e79aeadceba118b7bf23cb3715c
|
||||
- pull_tag: rocm/vllm:rocm7.0.0_vllm_0.10.2_20251006
|
||||
docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm7.0.0_vllm_0.10.2_20251006/images/sha256-94fd001964e1cf55c3224a445b1fb5be31a7dac302315255db8422d813edd7f5
|
||||
components:
|
||||
ROCm: 6.4.1
|
||||
vLLM: 0.10.1 (0.10.1rc2.dev409+g0b6bf6691.rocm641)
|
||||
PyTorch: 2.7.0+gitf717b2a
|
||||
hipBLASLt: 0.15
|
||||
ROCm: 7.0.0
|
||||
vLLM: 0.10.2 (0.11.0rc2.dev160+g790d22168.rocm700)
|
||||
PyTorch: 2.9.0a0+git1c57644
|
||||
hipBLASLt: 1.0.0
|
||||
dockerfile:
|
||||
commit: 790d22168820507f3105fef29596549378cfe399
|
||||
model_groups:
|
||||
- group: Meta Llama
|
||||
tag: llama
|
||||
models:
|
||||
- model: Llama 3.1 8B
|
||||
mad_tag: pyt_vllm_llama-3.1-8b
|
||||
model_repo: meta-llama/Llama-3.1-8B-Instruct
|
||||
url: https://huggingface.co/meta-llama/Llama-3.1-8B
|
||||
precision: float16
|
||||
config:
|
||||
tp: 1
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_seq_len_to_capture: 131072
|
||||
max_num_batched_tokens: 131072
|
||||
max_model_len: 8192
|
||||
- model: Llama 3.1 70B
|
||||
mad_tag: pyt_vllm_llama-3.1-70b
|
||||
model_repo: meta-llama/Llama-3.1-70B-Instruct
|
||||
url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
|
||||
precision: float16
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_seq_len_to_capture: 131072
|
||||
max_num_batched_tokens: 131072
|
||||
max_model_len: 8192
|
||||
- model: Llama 3.1 405B
|
||||
mad_tag: pyt_vllm_llama-3.1-405b
|
||||
model_repo: meta-llama/Llama-3.1-405B-Instruct
|
||||
url: https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct
|
||||
precision: float16
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_seq_len_to_capture: 131072
|
||||
max_num_batched_tokens: 131072
|
||||
max_model_len: 8192
|
||||
- model: Llama 2 70B
|
||||
mad_tag: pyt_vllm_llama-2-70b
|
||||
model_repo: meta-llama/Llama-2-70b-chat-hf
|
||||
url: https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
|
||||
precision: float16
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_seq_len_to_capture: 4096
|
||||
max_num_batched_tokens: 4096
|
||||
max_model_len: 4096
|
||||
- model: Llama 3.1 8B FP8
|
||||
mad_tag: pyt_vllm_llama-3.1-8b_fp8
|
||||
model_repo: amd/Llama-3.1-8B-Instruct-FP8-KV
|
||||
url: https://huggingface.co/amd/Llama-3.1-8B-Instruct-FP8-KV
|
||||
precision: float8
|
||||
config:
|
||||
tp: 1
|
||||
dtype: auto
|
||||
kv_cache_dtype: fp8
|
||||
max_seq_len_to_capture: 131072
|
||||
max_num_batched_tokens: 131072
|
||||
max_model_len: 8192
|
||||
- model: Llama 3.1 70B FP8
|
||||
mad_tag: pyt_vllm_llama-3.1-70b_fp8
|
||||
model_repo: amd/Llama-3.1-70B-Instruct-FP8-KV
|
||||
url: https://huggingface.co/amd/Llama-3.1-70B-Instruct-FP8-KV
|
||||
precision: float8
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: fp8
|
||||
max_seq_len_to_capture: 131072
|
||||
max_num_batched_tokens: 131072
|
||||
max_model_len: 8192
|
||||
- model: Llama 3.1 405B FP8
|
||||
mad_tag: pyt_vllm_llama-3.1-405b_fp8
|
||||
model_repo: amd/Llama-3.1-405B-Instruct-FP8-KV
|
||||
url: https://huggingface.co/amd/Llama-3.1-405B-Instruct-FP8-KV
|
||||
precision: float8
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: fp8
|
||||
max_seq_len_to_capture: 131072
|
||||
max_num_batched_tokens: 131072
|
||||
max_model_len: 8192
|
||||
- model: Llama 2 70B
|
||||
mad_tag: pyt_vllm_llama-2-70b
|
||||
model_repo: meta-llama/Llama-2-70b-chat-hf
|
||||
url: https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
|
||||
precision: float16
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_num_batched_tokens: 4096
|
||||
max_model_len: 4096
|
||||
- model: Llama 3.1 8B
|
||||
mad_tag: pyt_vllm_llama-3.1-8b
|
||||
model_repo: meta-llama/Llama-3.1-8B-Instruct
|
||||
url: https://huggingface.co/meta-llama/Llama-3.1-8B
|
||||
precision: float16
|
||||
config:
|
||||
tp: 1
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_num_batched_tokens: 131072
|
||||
max_model_len: 8192
|
||||
- model: Llama 3.1 8B FP8
|
||||
mad_tag: pyt_vllm_llama-3.1-8b_fp8
|
||||
model_repo: amd/Llama-3.1-8B-Instruct-FP8-KV
|
||||
url: https://huggingface.co/amd/Llama-3.1-8B-Instruct-FP8-KV
|
||||
precision: float8
|
||||
config:
|
||||
tp: 1
|
||||
dtype: auto
|
||||
kv_cache_dtype: fp8
|
||||
max_num_batched_tokens: 131072
|
||||
max_model_len: 8192
|
||||
- model: Llama 3.1 405B
|
||||
mad_tag: pyt_vllm_llama-3.1-405b
|
||||
model_repo: meta-llama/Llama-3.1-405B-Instruct
|
||||
url: https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct
|
||||
precision: float16
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_num_batched_tokens: 131072
|
||||
max_model_len: 8192
|
||||
- model: Llama 3.1 405B FP8
|
||||
mad_tag: pyt_vllm_llama-3.1-405b_fp8
|
||||
model_repo: amd/Llama-3.1-405B-Instruct-FP8-KV
|
||||
url: https://huggingface.co/amd/Llama-3.1-405B-Instruct-FP8-KV
|
||||
precision: float8
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: fp8
|
||||
max_num_batched_tokens: 131072
|
||||
max_model_len: 8192
|
||||
- model: Llama 3.1 405B MXFP4
|
||||
mad_tag: pyt_vllm_llama-3.1-405b_fp4
|
||||
model_repo: amd/Llama-3.1-405B-Instruct-MXFP4-Preview
|
||||
url: https://huggingface.co/amd/Llama-3.1-405B-Instruct-MXFP4-Preview
|
||||
precision: float4
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: fp8
|
||||
max_num_batched_tokens: 131072
|
||||
max_model_len: 8192
|
||||
- model: Llama 3.3 70B
|
||||
mad_tag: pyt_vllm_llama-3.3-70b
|
||||
model_repo: meta-llama/Llama-3.3-70B-Instruct
|
||||
url: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
|
||||
precision: float16
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_num_batched_tokens: 131072
|
||||
max_model_len: 8192
|
||||
- model: Llama 3.3 70B FP8
|
||||
mad_tag: pyt_vllm_llama-3.3-70b_fp8
|
||||
model_repo: amd/Llama-3.3-70B-Instruct-FP8-KV
|
||||
url: https://huggingface.co/amd/Llama-3.3-70B-Instruct-FP8-KV
|
||||
precision: float8
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: fp8
|
||||
max_num_batched_tokens: 131072
|
||||
max_model_len: 8192
|
||||
- model: Llama 3.3 70B MXFP4
|
||||
mad_tag: pyt_vllm_llama-3.3-70b_fp4
|
||||
model_repo: amd/Llama-3.3-70B-Instruct-MXFP4-Preview
|
||||
url: https://huggingface.co/amd/Llama-3.3-70B-Instruct-MXFP4-Preview
|
||||
precision: float4
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: fp8
|
||||
max_num_batched_tokens: 131072
|
||||
max_model_len: 8192
|
||||
- model: Llama 4 Scout 17Bx16E
|
||||
mad_tag: pyt_vllm_llama-4-scout-17b-16e
|
||||
model_repo: meta-llama/Llama-4-Scout-17B-16E-Instruct
|
||||
url: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct
|
||||
precision: float16
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_num_batched_tokens: 32768
|
||||
max_model_len: 8192
|
||||
- model: Llama 4 Maverick 17Bx128E
|
||||
mad_tag: pyt_vllm_llama-4-maverick-17b-128e
|
||||
model_repo: meta-llama/Llama-4-Maverick-17B-128E-Instruct
|
||||
url: https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-Instruct
|
||||
precision: float16
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_num_batched_tokens: 32768
|
||||
max_model_len: 8192
|
||||
- model: Llama 4 Maverick 17Bx128E FP8
|
||||
mad_tag: pyt_vllm_llama-4-maverick-17b-128e_fp8
|
||||
model_repo: meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
|
||||
url: https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
|
||||
precision: float8
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: fp8
|
||||
max_num_batched_tokens: 131072
|
||||
max_model_len: 8192
|
||||
- group: DeepSeek
|
||||
tag: deepseek
|
||||
models:
|
||||
- model: DeepSeek R1 0528 FP8
|
||||
mad_tag: pyt_vllm_deepseek-r1
|
||||
model_repo: deepseek-ai/DeepSeek-R1-0528
|
||||
url: https://huggingface.co/deepseek-ai/DeepSeek-R1-0528
|
||||
precision: float8
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: fp8
|
||||
max_num_seqs: 1024
|
||||
max_num_batched_tokens: 131072
|
||||
max_model_len: 8192
|
||||
- group: OpenAI GPT OSS
|
||||
tag: gpt-oss
|
||||
models:
|
||||
- model: GPT OSS 20B
|
||||
mad_tag: pyt_vllm_gpt-oss-20b
|
||||
model_repo: openai/gpt-oss-20b
|
||||
url: https://huggingface.co/openai/gpt-oss-20b
|
||||
precision: bfloat16
|
||||
config:
|
||||
tp: 1
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_num_batched_tokens: 8192
|
||||
max_model_len: 8192
|
||||
- model: GPT OSS 120B
|
||||
mad_tag: pyt_vllm_gpt-oss-120b
|
||||
model_repo: openai/gpt-oss-120b
|
||||
url: https://huggingface.co/openai/gpt-oss-120b
|
||||
precision: bfloat16
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_num_batched_tokens: 8192
|
||||
max_model_len: 8192
|
||||
- group: Mistral AI
|
||||
tag: mistral
|
||||
models:
|
||||
- model: Mixtral MoE 8x7B
|
||||
mad_tag: pyt_vllm_mixtral-8x7b
|
||||
model_repo: mistralai/Mixtral-8x7B-Instruct-v0.1
|
||||
url: https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1
|
||||
precision: float16
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_seq_len_to_capture: 32768
|
||||
max_num_batched_tokens: 32768
|
||||
max_model_len: 8192
|
||||
- model: Mixtral MoE 8x22B
|
||||
mad_tag: pyt_vllm_mixtral-8x22b
|
||||
model_repo: mistralai/Mixtral-8x22B-Instruct-v0.1
|
||||
url: https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1
|
||||
precision: float16
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_seq_len_to_capture: 65536
|
||||
max_num_batched_tokens: 65536
|
||||
max_model_len: 8192
|
||||
- model: Mixtral MoE 8x7B FP8
|
||||
mad_tag: pyt_vllm_mixtral-8x7b_fp8
|
||||
model_repo: amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
|
||||
url: https://huggingface.co/amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
|
||||
precision: float8
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: fp8
|
||||
max_seq_len_to_capture: 32768
|
||||
max_num_batched_tokens: 32768
|
||||
max_model_len: 8192
|
||||
- model: Mixtral MoE 8x22B FP8
|
||||
mad_tag: pyt_vllm_mixtral-8x22b_fp8
|
||||
model_repo: amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
|
||||
url: https://huggingface.co/amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
|
||||
precision: float8
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: fp8
|
||||
max_seq_len_to_capture: 65536
|
||||
max_num_batched_tokens: 65536
|
||||
max_model_len: 8192
|
||||
- model: Mixtral MoE 8x7B
|
||||
mad_tag: pyt_vllm_mixtral-8x7b
|
||||
model_repo: mistralai/Mixtral-8x7B-Instruct-v0.1
|
||||
url: https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1
|
||||
precision: float16
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_num_batched_tokens: 32768
|
||||
max_model_len: 8192
|
||||
- model: Mixtral MoE 8x7B FP8
|
||||
mad_tag: pyt_vllm_mixtral-8x7b_fp8
|
||||
model_repo: amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
|
||||
url: https://huggingface.co/amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
|
||||
precision: float8
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: fp8
|
||||
max_num_batched_tokens: 32768
|
||||
max_model_len: 8192
|
||||
- model: Mixtral MoE 8x22B
|
||||
mad_tag: pyt_vllm_mixtral-8x22b
|
||||
model_repo: mistralai/Mixtral-8x22B-Instruct-v0.1
|
||||
url: https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1
|
||||
precision: float16
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_num_batched_tokens: 65536
|
||||
max_model_len: 8192
|
||||
- model: Mixtral MoE 8x22B FP8
|
||||
mad_tag: pyt_vllm_mixtral-8x22b_fp8
|
||||
model_repo: amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
|
||||
url: https://huggingface.co/amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
|
||||
precision: float8
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: fp8
|
||||
max_num_batched_tokens: 65536
|
||||
max_model_len: 8192
|
||||
- group: Qwen
|
||||
tag: qwen
|
||||
models:
|
||||
- model: QwQ-32B
|
||||
mad_tag: pyt_vllm_qwq-32b
|
||||
model_repo: Qwen/QwQ-32B
|
||||
url: https://huggingface.co/Qwen/QwQ-32B
|
||||
precision: float16
|
||||
config:
|
||||
tp: 1
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_seq_len_to_capture: 131072
|
||||
max_num_batched_tokens: 131072
|
||||
max_model_len: 8192
|
||||
- model: Qwen3 30B A3B
|
||||
mad_tag: pyt_vllm_qwen3-30b-a3b
|
||||
model_repo: Qwen/Qwen3-30B-A3B
|
||||
url: https://huggingface.co/Qwen/Qwen3-30B-A3B
|
||||
precision: float16
|
||||
config:
|
||||
tp: 1
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_seq_len_to_capture: 32768
|
||||
max_num_batched_tokens: 32768
|
||||
max_model_len: 8192
|
||||
- model: Qwen3 8B
|
||||
mad_tag: pyt_vllm_qwen3-8b
|
||||
model_repo: Qwen/Qwen3-8B
|
||||
url: https://huggingface.co/Qwen/Qwen3-8B
|
||||
precision: float16
|
||||
config:
|
||||
tp: 1
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_num_batched_tokens: 40960
|
||||
max_model_len: 8192
|
||||
- model: Qwen3 32B
|
||||
mad_tag: pyt_vllm_qwen3-32b
|
||||
model_repo: Qwen/Qwen3-32b
|
||||
url: https://huggingface.co/Qwen/Qwen3-32B
|
||||
precision: float16
|
||||
config:
|
||||
tp: 1
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_num_batched_tokens: 40960
|
||||
max_model_len: 8192
|
||||
- model: Qwen3 30B A3B
|
||||
mad_tag: pyt_vllm_qwen3-30b-a3b
|
||||
model_repo: Qwen/Qwen3-30B-A3B
|
||||
url: https://huggingface.co/Qwen/Qwen3-30B-A3B
|
||||
precision: float16
|
||||
config:
|
||||
tp: 1
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_num_batched_tokens: 40960
|
||||
max_model_len: 8192
|
||||
- model: Qwen3 30B A3B FP8
|
||||
mad_tag: pyt_vllm_qwen3-30b-a3b_fp8
|
||||
model_repo: Qwen/Qwen3-30B-A3B-FP8
|
||||
url: https://huggingface.co/Qwen/Qwen3-30B-A3B-FP8
|
||||
precision: float16
|
||||
config:
|
||||
tp: 1
|
||||
dtype: auto
|
||||
kv_cache_dtype: fp8
|
||||
max_num_batched_tokens: 40960
|
||||
max_model_len: 8192
|
||||
- model: Qwen3 235B A22B
|
||||
mad_tag: pyt_vllm_qwen3-235b-a22b
|
||||
model_repo: Qwen/Qwen3-235B-A22B
|
||||
url: https://huggingface.co/Qwen/Qwen3-235B-A22B
|
||||
precision: float16
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_num_batched_tokens: 40960
|
||||
max_model_len: 8192
|
||||
- model: Qwen3 235B A22B FP8
|
||||
mad_tag: pyt_vllm_qwen3-235b-a22b_fp8
|
||||
model_repo: Qwen/Qwen3-235B-A22B-FP8
|
||||
url: https://huggingface.co/Qwen/Qwen3-235B-A22B-FP8
|
||||
precision: float8
|
||||
config:
|
||||
tp: 8
|
||||
dtype: auto
|
||||
kv_cache_dtype: fp8
|
||||
max_num_batched_tokens: 40960
|
||||
max_model_len: 8192
|
||||
- group: Microsoft Phi
|
||||
tag: phi
|
||||
models:
|
||||
- model: Phi-4
|
||||
mad_tag: pyt_vllm_phi-4
|
||||
model_repo: microsoft/phi-4
|
||||
url: https://huggingface.co/microsoft/phi-4
|
||||
config:
|
||||
tp: 1
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_seq_len_to_capture: 16384
|
||||
max_num_batched_tokens: 16384
|
||||
max_model_len: 8192
|
||||
- model: Phi-4
|
||||
mad_tag: pyt_vllm_phi-4
|
||||
model_repo: microsoft/phi-4
|
||||
url: https://huggingface.co/microsoft/phi-4
|
||||
precision: float16
|
||||
config:
|
||||
tp: 1
|
||||
dtype: auto
|
||||
kv_cache_dtype: auto
|
||||
max_num_batched_tokens: 16384
|
||||
max_model_len: 8192
|
||||
|
||||
@@ -0,0 +1,448 @@
|
||||
:orphan:
|
||||
|
||||
.. meta::
|
||||
:description: Learn how to validate LLM inference performance on MI300X accelerators using AMD MAD and the ROCm vLLM Docker image.
|
||||
:keywords: model, MAD, automation, dashboarding, validate
|
||||
|
||||
**********************************
|
||||
vLLM inference performance testing
|
||||
**********************************
|
||||
|
||||
.. caution::
|
||||
|
||||
This documentation does not reflect the latest version of ROCm vLLM
|
||||
inference performance documentation. See :doc:`../vllm` for the latest version.
|
||||
|
||||
.. _vllm-benchmark-unified-docker-909:
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.10.1_20250909-benchmark-models.yaml
|
||||
|
||||
{% set docker = data.dockers[0] %}
|
||||
|
||||
The `ROCm vLLM Docker <{{ docker.docker_hub_url }}>`_ image offers
|
||||
a prebuilt, optimized environment for validating large language model (LLM)
|
||||
inference performance on AMD Instinct™ MI300X series accelerators. This ROCm vLLM
|
||||
Docker image integrates vLLM and PyTorch tailored specifically for MI300X series
|
||||
accelerators and includes the following components:
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Software component
|
||||
- Version
|
||||
|
||||
{% for component_name, component_version in docker.components.items() %}
|
||||
* - {{ component_name }}
|
||||
- {{ component_version }}
|
||||
{% endfor %}
|
||||
|
||||
With this Docker image, you can quickly test the :ref:`expected
|
||||
inference performance numbers <vllm-benchmark-performance-measurements-909>` for
|
||||
MI300X series accelerators.
|
||||
|
||||
What's new
|
||||
==========
|
||||
|
||||
The following is summary of notable changes since the :doc:`previous ROCm/vLLM Docker release <vllm-history>`.
|
||||
|
||||
* Upgraded to vLLM v0.10.1.
|
||||
|
||||
* Set ``VLLM_V1_USE_PREFILL_DECODE_ATTENTION=1`` by default for better performance.
|
||||
|
||||
* Set ``VLLM_ROCM_USE_AITER_RMSNORM=0`` by default to avoid various issues with torch compile.
|
||||
|
||||
.. _vllm-benchmark-supported-models-909:
|
||||
|
||||
Supported models
|
||||
================
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.10.1_20250909-benchmark-models.yaml
|
||||
|
||||
{% set docker = data.dockers[0] %}
|
||||
{% set model_groups = data.model_groups %}
|
||||
|
||||
.. _vllm-benchmark-available-models-909:
|
||||
|
||||
The following models are supported for inference performance benchmarking
|
||||
with vLLM and ROCm. Some instructions, commands, and recommendations in this
|
||||
documentation might vary by model -- select one to get started.
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<div id="vllm-benchmark-ud-params-picker" class="container-fluid">
|
||||
<div class="row gx-0">
|
||||
<div class="col-2 me-1 px-2 model-param-head">Model</div>
|
||||
<div class="row col-10 pe-0">
|
||||
{% for model_group in model_groups %}
|
||||
<div class="col-3 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="row gx-0 pt-1">
|
||||
<div class="col-2 me-1 px-2 model-param-head">Variant</div>
|
||||
<div class="row col-10 pe-0">
|
||||
{% for model_group in model_groups %}
|
||||
{% set models = model_group.models %}
|
||||
{% for model in models %}
|
||||
{% if models|length % 3 == 0 %}
|
||||
<div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
|
||||
{% else %}
|
||||
<div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
|
||||
{% endif %}
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
.. _vllm-benchmark-vllm-909:
|
||||
|
||||
{% for model_group in model_groups %}
|
||||
{% for model in model_group.models %}
|
||||
|
||||
.. container:: model-doc {{ model.mad_tag }}
|
||||
|
||||
.. note::
|
||||
|
||||
See the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_ to learn more about your selected model.
|
||||
Some models require access authorization prior to use via an external license agreement through a third party.
|
||||
{% if model.precision == "float8" and model.model_repo.startswith("amd") %}
|
||||
This model uses FP8 quantization via `AMD Quark <https://quark.docs.amd.com/latest/>`__ for efficient inference on AMD accelerators.
|
||||
{% endif %}
|
||||
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
|
||||
.. _vllm-benchmark-performance-measurements-909:
|
||||
|
||||
Performance measurements
|
||||
========================
|
||||
|
||||
To evaluate performance, the
|
||||
`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
|
||||
page provides reference throughput and serving measurements for inferencing popular AI models.
|
||||
|
||||
.. important::
|
||||
|
||||
The performance data presented in
|
||||
`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
|
||||
only reflects the latest version of this inference benchmarking environment.
|
||||
The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.
|
||||
|
||||
System validation
|
||||
=================
|
||||
|
||||
Before running AI workloads, it's important to validate that your AMD hardware is configured
|
||||
correctly and performing optimally.
|
||||
|
||||
If you have already validated your system settings, including aspects like NUMA auto-balancing, you
|
||||
can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
|
||||
optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
|
||||
before starting training.
|
||||
|
||||
To test for optimal performance, consult the recommended :ref:`System health benchmarks
|
||||
<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
|
||||
system's configuration.
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.10.1_20250909-benchmark-models.yaml
|
||||
|
||||
{% set docker = data.dockers[0] %}
|
||||
{% set model_groups = data.model_groups %}
|
||||
|
||||
Pull the Docker image
|
||||
=====================
|
||||
|
||||
Download the `ROCm vLLM Docker image <{{ docker.docker_hub_url }}>`_.
|
||||
Use the following command to pull the Docker image from Docker Hub.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
docker pull {{ docker.pull_tag }}
|
||||
|
||||
Benchmarking
|
||||
============
|
||||
|
||||
Once the setup is complete, choose between two options to reproduce the
|
||||
benchmark results:
|
||||
|
||||
.. _vllm-benchmark-mad-909:
|
||||
|
||||
{% for model_group in model_groups %}
|
||||
{% for model in model_group.models %}
|
||||
|
||||
.. container:: model-doc {{model.mad_tag}}
|
||||
|
||||
.. tab-set::
|
||||
|
||||
.. tab-item:: MAD-integrated benchmarking
|
||||
|
||||
The following run command is tailored to {{ model.model }}.
|
||||
See :ref:`vllm-benchmark-supported-models-909` to switch to another available model.
|
||||
|
||||
1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
|
||||
directory and install the required packages on the host machine.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
git clone https://github.com/ROCm/MAD
|
||||
cd MAD
|
||||
pip install -r requirements.txt
|
||||
|
||||
2. Use this command to run the performance benchmark test on the `{{model.model}} <{{ model.url }}>`_ model
|
||||
using one GPU with the :literal:`{{model.precision}}` data type on the host machine.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
|
||||
madengine run \
|
||||
--tags {{model.mad_tag}} \
|
||||
--keep-model-dir \
|
||||
--live-output \
|
||||
--timeout 28800
|
||||
|
||||
MAD launches a Docker container with the name
|
||||
``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
|
||||
model are collected in the following paths: ``{{ model.mad_tag }}_throughput.csv``
|
||||
and ``{{ model.mad_tag }}_serving.csv``.
|
||||
|
||||
Although the :ref:`available models
|
||||
<vllm-benchmark-available-models-909>` are preconfigured to collect
|
||||
offline throughput and online serving performance data, you can
|
||||
also change the benchmarking parameters. See the standalone
|
||||
benchmarking tab for more information.
|
||||
|
||||
{% if model.tunableop %}
|
||||
|
||||
.. note::
|
||||
|
||||
For improved performance, consider enabling :ref:`PyTorch TunableOp <mi300x-tunableop>`.
|
||||
TunableOp automatically explores different implementations and configurations of certain PyTorch
|
||||
operators to find the fastest one for your hardware.
|
||||
|
||||
By default, ``{{model.mad_tag}}`` runs with TunableOp disabled (see
|
||||
`<https://github.com/ROCm/MAD/blob/develop/models.json>`__). To enable it, include
|
||||
the ``--tunableop on`` argument in your run.
|
||||
|
||||
Enabling TunableOp triggers a two-pass run -- a warm-up followed by the
|
||||
performance-collection run.
|
||||
|
||||
{% endif %}
|
||||
|
||||
.. tab-item:: Standalone benchmarking
|
||||
|
||||
The following commands are optimized for {{ model.model }}.
|
||||
See :ref:`vllm-benchmark-supported-models-909` to switch to another available model.
|
||||
|
||||
.. seealso::
|
||||
|
||||
For more information on configuration, see the `config files
|
||||
<https://github.com/ROCm/MAD/tree/develop/scripts/vllm/configs>`__
|
||||
in the MAD repository. Refer to the `vLLM engine <https://docs.vllm.ai/en/latest/configuration/engine_args.html#engineargs>`__
|
||||
for descriptions of available configuration options
|
||||
and `Benchmarking vLLM <https://github.com/vllm-project/vllm/blob/main/benchmarks/README.md>`__ for
|
||||
additional benchmarking information.
|
||||
|
||||
.. rubric:: Launch the container
|
||||
|
||||
You can run the vLLM benchmark tool independently by starting the
|
||||
`Docker container <{{ docker.docker_hub_url }}>`_ as shown
|
||||
in the following snippet.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
docker pull {{ docker.pull_tag }}
|
||||
docker run -it \
|
||||
--device=/dev/kfd \
|
||||
--device=/dev/dri \
|
||||
--group-add video \
|
||||
--shm-size 16G \
|
||||
--security-opt seccomp=unconfined \
|
||||
--security-opt apparmor=unconfined \
|
||||
--cap-add=SYS_PTRACE \
|
||||
-v $(pwd):/workspace \
|
||||
--env HUGGINGFACE_HUB_CACHE=/workspace \
|
||||
--name test \
|
||||
{{ docker.pull_tag }}
|
||||
|
||||
.. rubric:: Throughput command
|
||||
|
||||
Use the following command to start the throughput benchmark.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
model={{ model.model_repo }}
|
||||
tp={{ model.config.tp }}
|
||||
num_prompts=1024
|
||||
in=128
|
||||
out=128
|
||||
dtype={{ model.config.dtype }}
|
||||
kv_cache_dtype={{ model.config.kv_cache_dtype }}
|
||||
max_num_seqs=1024
|
||||
max_seq_len_to_capture={{ model.config.max_seq_len_to_capture }}
|
||||
max_num_batched_tokens={{ model.config.max_num_batched_tokens }}
|
||||
max_model_len={{ model.config.max_model_len }}
|
||||
|
||||
vllm bench throughput --model $model \
|
||||
-tp $tp \
|
||||
--num-prompts $num_prompts \
|
||||
--input-len $in \
|
||||
--output-len $out \
|
||||
--dtype $dtype \
|
||||
--kv-cache-dtype $kv_cache_dtype \
|
||||
--max-num-seqs $max_num_seqs \
|
||||
--max-seq-len-to-capture $max_seq_len_to_capture \
|
||||
--max-num-batched-tokens $max_num_batched_tokens \
|
||||
--max-model-len $max_model_len \
|
||||
--trust-remote-code \
|
||||
--output-json ${model}_throughput.json \
|
||||
--gpu-memory-utilization 0.9
|
||||
|
||||
.. rubric:: Serving command
|
||||
|
||||
1. Start the server using the following command:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
model={{ model.model_repo }}
|
||||
tp={{ model.config.tp }}
|
||||
dtype={{ model.config.dtype }}
|
||||
kv_cache_dtype={{ model.config.kv_cache_dtype }}
|
||||
max_num_seqs=256
|
||||
max_seq_len_to_capture={{ model.config.max_seq_len_to_capture }}
|
||||
max_num_batched_tokens={{ model.config.max_num_batched_tokens }}
|
||||
max_model_len={{ model.config.max_model_len }}
|
||||
|
||||
vllm serve $model \
|
||||
-tp $tp \
|
||||
--dtype $dtype \
|
||||
--kv-cache-dtype $kv_cache_dtype \
|
||||
--max-num-seqs $max_num_seqs \
|
||||
--max-seq-len-to-capture $max_seq_len_to_capture \
|
||||
--max-num-batched-tokens $max_num_batched_tokens \
|
||||
--max-model-len $max_model_len \
|
||||
--no-enable-prefix-caching \
|
||||
--swap-space 16 \
|
||||
--disable-log-requests \
|
||||
--trust-remote-code \
|
||||
--gpu-memory-utilization 0.9
|
||||
|
||||
Wait until the model has loaded and the server is ready to accept requests.
|
||||
|
||||
2. On another terminal on the same machine, run the benchmark:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
# Connect to the container
|
||||
docker exec -it test bash
|
||||
|
||||
# Wait for the server to start
|
||||
until curl -s http://localhost:8000/v1/models; do sleep 30; done
|
||||
|
||||
# Run the benchmark
|
||||
model={{ model.model_repo }}
|
||||
max_concurrency=1
|
||||
num_prompts=10
|
||||
in=128
|
||||
out=128
|
||||
vllm bench serve --model $model \
|
||||
--percentile-metrics "ttft,tpot,itl,e2el" \
|
||||
--dataset-name random \
|
||||
--ignore-eos \
|
||||
--max-concurrency $max_concurrency \
|
||||
--num-prompts $num_prompts \
|
||||
--random-input-len $in \
|
||||
--random-output-len $out \
|
||||
--trust-remote-code \
|
||||
--save-result \
|
||||
--result-filename ${model}_serving.json
|
||||
|
||||
.. note::
|
||||
|
||||
For improved performance with certain Mixture of Experts models, such as Mixtral 8x22B,
|
||||
try adding ``export VLLM_ROCM_USE_AITER=1`` to your commands.
|
||||
|
||||
If you encounter the following error, pass your access-authorized Hugging
|
||||
Face token to the gated models.
|
||||
|
||||
.. code-block::
|
||||
|
||||
OSError: You are trying to access a gated repo.
|
||||
|
||||
# pass your HF_TOKEN
|
||||
export HF_TOKEN=$your_personal_hf_token
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<style>
|
||||
mjx-container[jax="CHTML"][display="true"] {
|
||||
text-align: left;
|
||||
margin: 0;
|
||||
}
|
||||
</style>
|
||||
|
||||
.. note::
|
||||
|
||||
Throughput is calculated as:
|
||||
|
||||
- .. math:: throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time
|
||||
|
||||
- .. math:: throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
|
||||
Advanced usage
|
||||
==============
|
||||
|
||||
For information on experimental features and known issues related to ROCm optimization efforts on vLLM,
|
||||
see the developer's guide at `<https://github.com/ROCm/vllm/blob/documentation/docs/dev-docker/README.md>`__.
|
||||
|
||||
Reproducing the Docker image
|
||||
----------------------------
|
||||
|
||||
To reproduce this ROCm/vLLM Docker image release, follow these steps:
|
||||
|
||||
1. Clone the `vLLM repository <https://github.com/ROCm/vllm>`__.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
git clone https://github.com/ROCm/vllm.git
|
||||
|
||||
2. Checkout the specific release commit.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
cd vllm
|
||||
git checkout 6663000a391911eba96d7864a26ac42b07f6ef29
|
||||
|
||||
3. Build the Docker image. Replace ``vllm-rocm`` with your desired image tag.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
docker build -f docker/Dockerfile.rocm -t vllm-rocm .
|
||||
|
||||
Further reading
|
||||
===============
|
||||
|
||||
- To learn more about the options for latency and throughput benchmark scripts,
|
||||
see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
|
||||
|
||||
- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
|
||||
|
||||
- To learn more about system settings and management practices to configure your system for
|
||||
AMD Instinct MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
|
||||
|
||||
- See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
|
||||
a brief introduction to vLLM and optimization strategies.
|
||||
|
||||
- For application performance optimization strategies for HPC and AI workloads,
|
||||
including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
|
||||
|
||||
- For a list of other ready-made Docker images for AI with ROCm, see
|
||||
`AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
|
||||
|
||||
Previous versions
|
||||
=================
|
||||
|
||||
See :doc:`vllm-history` to find documentation for previous releases
|
||||
of the ``ROCm/vllm`` Docker image.
|
||||
@@ -7,7 +7,7 @@ vLLM inference performance testing version history
|
||||
This table lists previous versions of the ROCm vLLM inference Docker image for
|
||||
inference performance testing. For detailed information about available models
|
||||
for benchmarking, see the version-specific documentation. You can find tagged
|
||||
previous releases of the ``ROCm/vllm`` Docker image on `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.10.1_20250909/images/sha256-1113268572e26d59b205792047bea0e61e018e79aeadceba118b7bf23cb3715c>`__.
|
||||
previous releases of the ``ROCm/vllm`` Docker image on `Docker Hub <https://hub.docker.com/r/rocm/vllm/tags>`__.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
@@ -16,14 +16,23 @@ previous releases of the ``ROCm/vllm`` Docker image on `Docker Hub <https://hub.
|
||||
- Components
|
||||
- Resources
|
||||
|
||||
* - ``rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909``
|
||||
* - ``rocm/vllm:rocm7.0.0_vllm_0.10.2_20251006``
|
||||
(latest)
|
||||
-
|
||||
* ROCm 7.0.0
|
||||
* vLLM 0.10.2
|
||||
* PyTorch 2.9.0
|
||||
-
|
||||
* :doc:`Documentation <../vllm>`
|
||||
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm7.0.0_vllm_0.10.2_20251006/images/sha256-94fd001964e1cf55c3224a445b1fb5be31a7dac302315255db8422d813edd7f5>`__
|
||||
|
||||
* - ``rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909``
|
||||
-
|
||||
* ROCm 6.4.1
|
||||
* vLLM 0.10.1
|
||||
* PyTorch 2.7.0
|
||||
-
|
||||
* :doc:`Documentation <../vllm>`
|
||||
* :doc:`Documentation <vllm-0.10.1-20250909>`
|
||||
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.10.1_20250909/images/sha256-1113268572e26d59b205792047bea0e61e018e79aeadceba118b7bf23cb3715c>`__
|
||||
|
||||
* - ``rocm/vllm:rocm6.4.1_vllm_0.10.0_20250812``
|
||||
|
||||
@@ -6,45 +6,63 @@
|
||||
vLLM inference performance testing
|
||||
**********************************
|
||||
|
||||
.. _vllm-benchmark-unified-docker-909:
|
||||
.. _vllm-benchmark-unified-docker-930:
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
|
||||
|
||||
{% set docker = data.dockers[0] %}
|
||||
|
||||
The `ROCm vLLM Docker <{{ docker.docker_hub_url }}>`_ image offers
|
||||
a prebuilt, optimized environment for validating large language model (LLM)
|
||||
inference performance on AMD Instinct™ MI300X series GPUs. This ROCm vLLM
|
||||
Docker image integrates vLLM and PyTorch tailored specifically for MI300X series
|
||||
GPUs and includes the following components:
|
||||
The `ROCm vLLM Docker <{{ docker.docker_hub_url }}>`_ image offers a
|
||||
prebuilt, optimized environment for validating large language model (LLM)
|
||||
inference performance on AMD Instinct™ MI355X, MI350X, MI325X and MI300X
|
||||
GPUs. This ROCm vLLM Docker image integrates vLLM and PyTorch tailored
|
||||
specifically for AMD data center GPUs and includes the following components:
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
.. tab-set::
|
||||
|
||||
* - Software component
|
||||
- Version
|
||||
.. tab-item:: {{ docker.pull_tag }}
|
||||
|
||||
{% for component_name, component_version in docker.components.items() %}
|
||||
* - {{ component_name }}
|
||||
- {{ component_version }}
|
||||
{% endfor %}
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Software component
|
||||
- Version
|
||||
|
||||
{% for component_name, component_version in docker.components.items() %}
|
||||
* - {{ component_name }}
|
||||
- {{ component_version }}
|
||||
{% endfor %}
|
||||
|
||||
With this Docker image, you can quickly test the :ref:`expected
|
||||
inference performance numbers <vllm-benchmark-performance-measurements-909>` for
|
||||
MI300X series GPUs.
|
||||
inference performance numbers <vllm-benchmark-performance-measurements-930>` for
|
||||
AMD Instinct GPUs.
|
||||
|
||||
What's new
|
||||
==========
|
||||
|
||||
The following is summary of notable changes since the :doc:`previous ROCm/vLLM Docker release <previous-versions/vllm-history>`.
|
||||
|
||||
* Upgraded to vLLM v0.10.1.
|
||||
* Added support for AMD Instinct MI355X and MI350X GPUs.
|
||||
|
||||
* Set ``VLLM_V1_USE_PREFILL_DECODE_ATTENTION=1`` by default for better performance.
|
||||
* Added support and benchmarking instructions for the following models. See :ref:`vllm-benchmark-supported-models-930`.
|
||||
|
||||
* Set ``VLLM_ROCM_USE_AITER_RMSNORM=0`` by default to avoid various issues with torch compile.
|
||||
* Llama 4 Scout and Maverick
|
||||
|
||||
.. _vllm-benchmark-supported-models-909:
|
||||
* DeepSeek R1 0528 FP8
|
||||
|
||||
* MXFP4 models (MI355X and MI350X only): Llama 3.3 70B MXFP4 and Llama 3.1 405B MXFP4
|
||||
|
||||
* GPT OSS 20B and 120B
|
||||
|
||||
* Qwen 3 32B, 30B-A3B, and 235B-A22B
|
||||
|
||||
* Removed the deprecated ``--max-seq-len-to-capture`` flag.
|
||||
|
||||
* ``--gpu-memory-utilization`` is now configurable via the `configuration files
|
||||
<https://github.com/ROCm/MAD/tree/develop/scripts/vllm/configs>`__ in the MAD
|
||||
repository.
|
||||
|
||||
.. _vllm-benchmark-supported-models-930:
|
||||
|
||||
Supported models
|
||||
================
|
||||
@@ -54,11 +72,12 @@ Supported models
|
||||
{% set docker = data.dockers[0] %}
|
||||
{% set model_groups = data.model_groups %}
|
||||
|
||||
.. _vllm-benchmark-available-models-909:
|
||||
.. _vllm-benchmark-available-models-930:
|
||||
|
||||
The following models are supported for inference performance benchmarking
|
||||
with vLLM and ROCm. Some instructions, commands, and recommendations in this
|
||||
documentation might vary by model -- select one to get started.
|
||||
documentation might vary by model -- select one to get started. MXFP4 models
|
||||
are only supported on MI355X and MI350X GPUs.
|
||||
|
||||
.. raw:: html
|
||||
|
||||
@@ -67,7 +86,7 @@ Supported models
|
||||
<div class="col-2 me-1 px-2 model-param-head">Model</div>
|
||||
<div class="row col-10 pe-0">
|
||||
{% for model_group in model_groups %}
|
||||
<div class="col-3 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
|
||||
<div class="col-4 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
@@ -89,13 +108,20 @@ Supported models
|
||||
</div>
|
||||
</div>
|
||||
|
||||
.. _vllm-benchmark-vllm-909:
|
||||
.. _vllm-benchmark-vllm-930:
|
||||
|
||||
{% for model_group in model_groups %}
|
||||
{% for model in model_group.models %}
|
||||
|
||||
.. container:: model-doc {{ model.mad_tag }}
|
||||
|
||||
|
||||
{% if model.precision == "float4" %}
|
||||
.. important::
|
||||
|
||||
MXFP4 is supported only on MI355X and MI350X GPUs.
|
||||
{% endif %}
|
||||
|
||||
.. note::
|
||||
|
||||
See the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_ to learn more about your selected model.
|
||||
@@ -103,11 +129,14 @@ Supported models
|
||||
{% if model.precision == "float8" and model.model_repo.startswith("amd") %}
|
||||
This model uses FP8 quantization via `AMD Quark <https://quark.docs.amd.com/latest/>`__ for efficient inference on AMD GPUs.
|
||||
{% endif %}
|
||||
{% if model.precision == "float4" and model.model_repo.startswith("amd") %}
|
||||
This model uses FP4 quantization via `AMD Quark <https://quark.docs.amd.com/latest/>`__ for efficient inference on AMD GPUs.
|
||||
{% endif %}
|
||||
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
|
||||
.. _vllm-benchmark-performance-measurements-909:
|
||||
.. _vllm-benchmark-performance-measurements-930:
|
||||
|
||||
Performance measurements
|
||||
========================
|
||||
@@ -121,7 +150,7 @@ page provides reference throughput and serving measurements for inferencing popu
|
||||
The performance data presented in
|
||||
`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
|
||||
only reflects the latest version of this inference benchmarking environment.
|
||||
The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X GPUs or ROCm software.
|
||||
The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct GPUs or ROCm software.
|
||||
|
||||
System validation
|
||||
=================
|
||||
@@ -163,7 +192,7 @@ Benchmarking
|
||||
Once the setup is complete, choose between two options to reproduce the
|
||||
benchmark results:
|
||||
|
||||
.. _vllm-benchmark-mad-909:
|
||||
.. _vllm-benchmark-mad-930:
|
||||
|
||||
{% for model_group in model_groups %}
|
||||
{% for model in model_group.models %}
|
||||
@@ -175,7 +204,7 @@ Benchmarking
|
||||
.. tab-item:: MAD-integrated benchmarking
|
||||
|
||||
The following run command is tailored to {{ model.model }}.
|
||||
See :ref:`vllm-benchmark-supported-models-909` to switch to another available model.
|
||||
See :ref:`vllm-benchmark-supported-models-930` to switch to another available model.
|
||||
|
||||
1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
|
||||
directory and install the required packages on the host machine.
|
||||
@@ -186,8 +215,9 @@ Benchmarking
|
||||
cd MAD
|
||||
pip install -r requirements.txt
|
||||
|
||||
2. Use this command to run the performance benchmark test on the `{{model.model}} <{{ model.url }}>`_ model
|
||||
using one GPU with the :literal:`{{model.precision}}` data type on the host machine.
|
||||
2. On the host machine, use this command to run the performance benchmark test on
|
||||
the `{{model.model}} <{{ model.url }}>`_ model using one node with the
|
||||
:literal:`{{model.precision}}` data type.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
@@ -195,8 +225,7 @@ Benchmarking
|
||||
madengine run \
|
||||
--tags {{model.mad_tag}} \
|
||||
--keep-model-dir \
|
||||
--live-output \
|
||||
--timeout 28800
|
||||
--live-output
|
||||
|
||||
MAD launches a Docker container with the name
|
||||
``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
|
||||
@@ -204,7 +233,7 @@ Benchmarking
|
||||
and ``{{ model.mad_tag }}_serving.csv``.
|
||||
|
||||
Although the :ref:`available models
|
||||
<vllm-benchmark-available-models-909>` are preconfigured to collect
|
||||
<vllm-benchmark-available-models-930>` are preconfigured to collect
|
||||
offline throughput and online serving performance data, you can
|
||||
also change the benchmarking parameters. See the standalone
|
||||
benchmarking tab for more information.
|
||||
@@ -229,7 +258,7 @@ Benchmarking
|
||||
.. tab-item:: Standalone benchmarking
|
||||
|
||||
The following commands are optimized for {{ model.model }}.
|
||||
See :ref:`vllm-benchmark-supported-models-909` to switch to another available model.
|
||||
See :ref:`vllm-benchmark-supported-models-930` to switch to another available model.
|
||||
|
||||
.. seealso::
|
||||
|
||||
@@ -270,13 +299,12 @@ Benchmarking
|
||||
|
||||
model={{ model.model_repo }}
|
||||
tp={{ model.config.tp }}
|
||||
num_prompts=1024
|
||||
in=128
|
||||
out=128
|
||||
dtype={{ model.config.dtype }}
|
||||
num_prompts={{ model.config.num_prompts | default(1024) }}
|
||||
in={{ model.config.in | default(128) }}
|
||||
out={{ model.config.in | default(128) }}
|
||||
dtype={{ model.config.dtype | default("auto") }}
|
||||
kv_cache_dtype={{ model.config.kv_cache_dtype }}
|
||||
max_num_seqs=1024
|
||||
max_seq_len_to_capture={{ model.config.max_seq_len_to_capture }}
|
||||
max_num_seqs={{ model.config.max_num_seqs | default(1024) }}
|
||||
max_num_batched_tokens={{ model.config.max_num_batched_tokens }}
|
||||
max_model_len={{ model.config.max_model_len }}
|
||||
|
||||
@@ -288,12 +316,11 @@ Benchmarking
|
||||
--dtype $dtype \
|
||||
--kv-cache-dtype $kv_cache_dtype \
|
||||
--max-num-seqs $max_num_seqs \
|
||||
--max-seq-len-to-capture $max_seq_len_to_capture \
|
||||
--max-num-batched-tokens $max_num_batched_tokens \
|
||||
--max-model-len $max_model_len \
|
||||
--trust-remote-code \
|
||||
--output-json ${model}_throughput.json \
|
||||
--gpu-memory-utilization 0.9
|
||||
--gpu-memory-utilization {{ model.config.gpu_memory_utilization | default(0.9) }}
|
||||
|
||||
.. rubric:: Serving command
|
||||
|
||||
@@ -306,7 +333,6 @@ Benchmarking
|
||||
dtype={{ model.config.dtype }}
|
||||
kv_cache_dtype={{ model.config.kv_cache_dtype }}
|
||||
max_num_seqs=256
|
||||
max_seq_len_to_capture={{ model.config.max_seq_len_to_capture }}
|
||||
max_num_batched_tokens={{ model.config.max_num_batched_tokens }}
|
||||
max_model_len={{ model.config.max_model_len }}
|
||||
|
||||
@@ -315,7 +341,6 @@ Benchmarking
|
||||
--dtype $dtype \
|
||||
--kv-cache-dtype $kv_cache_dtype \
|
||||
--max-num-seqs $max_num_seqs \
|
||||
--max-seq-len-to-capture $max_seq_len_to_capture \
|
||||
--max-num-batched-tokens $max_num_batched_tokens \
|
||||
--max-model-len $max_model_len \
|
||||
--no-enable-prefix-caching \
|
||||
@@ -397,26 +422,31 @@ see the developer's guide at `<https://github.com/ROCm/vllm/blob/documentation/d
|
||||
Reproducing the Docker image
|
||||
----------------------------
|
||||
|
||||
To reproduce this ROCm/vLLM Docker image release, follow these steps:
|
||||
To reproduce this ROCm-enabled vLLM Docker image release, follow these steps:
|
||||
|
||||
1. Clone the `vLLM repository <https://github.com/ROCm/vllm>`__.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
git clone https://github.com/ROCm/vllm.git
|
||||
|
||||
2. Checkout the specific release commit.
|
||||
1. Clone the `vLLM repository <https://github.com/vllm-project/vllm>`__.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
git clone https://github.com/vllm-project/vllm.git
|
||||
cd vllm
|
||||
git checkout 6663000a391911eba96d7864a26ac42b07f6ef29
|
||||
|
||||
3. Build the Docker image. Replace ``vllm-rocm`` with your desired image tag.
|
||||
2. Use the following command to build the image directly from the specified commit.
|
||||
|
||||
.. code-block:: shell
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
|
||||
|
||||
docker build -f docker/Dockerfile.rocm -t vllm-rocm .
|
||||
{% set docker = data.dockers[0] %}
|
||||
.. code-block:: shell
|
||||
|
||||
docker build -f docker/Dockerfile.rocm \
|
||||
--build-arg REMOTE_VLLM=1 \
|
||||
--build-arg VLLM_REPO=https://github.com/ROCm/vllm \
|
||||
--build-arg VLLM_BRANCH="{{ docker.dockerfile.commit }}" \
|
||||
-t vllm-rocm .
|
||||
|
||||
.. tip::
|
||||
|
||||
Replace ``vllm-rocm`` with your desired image tag.
|
||||
|
||||
Further reading
|
||||
===============
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
rocm-docs-core==1.20.1
|
||||
rocm-docs-core==1.26.0
|
||||
sphinx-reredirects
|
||||
sphinx-sitemap
|
||||
sphinxcontrib.datatemplates==0.11.0
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
# This file is autogenerated by pip-compile with Python 3.10
|
||||
# by the following command:
|
||||
#
|
||||
# pip-compile requirements.in
|
||||
# pip-compile docs/sphinx/requirements.in
|
||||
#
|
||||
accessible-pygments==0.0.5
|
||||
# via pydata-sphinx-theme
|
||||
@@ -10,7 +10,7 @@ alabaster==1.0.0
|
||||
# via sphinx
|
||||
asttokens==3.0.0
|
||||
# via stack-data
|
||||
attrs==25.3.0
|
||||
attrs==25.4.0
|
||||
# via
|
||||
# jsonschema
|
||||
# jupyter-cache
|
||||
@@ -19,34 +19,32 @@ babel==2.17.0
|
||||
# via
|
||||
# pydata-sphinx-theme
|
||||
# sphinx
|
||||
beautifulsoup4==4.13.4
|
||||
beautifulsoup4==4.14.2
|
||||
# via pydata-sphinx-theme
|
||||
breathe==4.36.0
|
||||
# via rocm-docs-core
|
||||
certifi==2025.4.26
|
||||
certifi==2025.10.5
|
||||
# via requests
|
||||
cffi==1.17.1
|
||||
cffi==2.0.0
|
||||
# via
|
||||
# cryptography
|
||||
# pynacl
|
||||
charset-normalizer==3.4.2
|
||||
charset-normalizer==3.4.3
|
||||
# via requests
|
||||
click==8.2.1
|
||||
click==8.3.0
|
||||
# via
|
||||
# jupyter-cache
|
||||
# sphinx-external-toc
|
||||
comm==0.2.2
|
||||
comm==0.2.3
|
||||
# via ipykernel
|
||||
cryptography==45.0.3
|
||||
cryptography==46.0.2
|
||||
# via pyjwt
|
||||
debugpy==1.8.14
|
||||
debugpy==1.8.17
|
||||
# via ipykernel
|
||||
decorator==5.2.1
|
||||
# via ipython
|
||||
defusedxml==0.7.1
|
||||
# via sphinxcontrib-datatemplates
|
||||
deprecated==1.2.18
|
||||
# via pygithub
|
||||
docutils==0.21.2
|
||||
# via
|
||||
# myst-parser
|
||||
@@ -54,17 +52,17 @@ docutils==0.21.2
|
||||
# sphinx
|
||||
exceptiongroup==1.3.0
|
||||
# via ipython
|
||||
executing==2.2.0
|
||||
executing==2.2.1
|
||||
# via stack-data
|
||||
fastjsonschema==2.21.1
|
||||
fastjsonschema==2.21.2
|
||||
# via
|
||||
# nbformat
|
||||
# rocm-docs-core
|
||||
gitdb==4.0.12
|
||||
# via gitpython
|
||||
gitpython==3.1.44
|
||||
gitpython==3.1.45
|
||||
# via rocm-docs-core
|
||||
greenlet==3.2.3
|
||||
greenlet==3.2.4
|
||||
# via sqlalchemy
|
||||
idna==3.10
|
||||
# via requests
|
||||
@@ -74,7 +72,7 @@ importlib-metadata==8.7.0
|
||||
# via
|
||||
# jupyter-cache
|
||||
# myst-nb
|
||||
ipykernel==6.29.5
|
||||
ipykernel==6.30.1
|
||||
# via myst-nb
|
||||
ipython==8.37.0
|
||||
# via
|
||||
@@ -86,9 +84,9 @@ jinja2==3.1.6
|
||||
# via
|
||||
# myst-parser
|
||||
# sphinx
|
||||
jsonschema==4.24.0
|
||||
jsonschema==4.25.1
|
||||
# via nbformat
|
||||
jsonschema-specifications==2025.4.1
|
||||
jsonschema-specifications==2025.9.1
|
||||
# via jsonschema
|
||||
jupyter-cache==1.0.1
|
||||
# via myst-nb
|
||||
@@ -106,17 +104,17 @@ markdown-it-py==3.0.0
|
||||
# via
|
||||
# mdit-py-plugins
|
||||
# myst-parser
|
||||
markupsafe==3.0.2
|
||||
markupsafe==3.0.3
|
||||
# via jinja2
|
||||
matplotlib-inline==0.1.7
|
||||
# via
|
||||
# ipykernel
|
||||
# ipython
|
||||
mdit-py-plugins==0.4.2
|
||||
mdit-py-plugins==0.5.0
|
||||
# via myst-parser
|
||||
mdurl==0.1.2
|
||||
# via markdown-it-py
|
||||
myst-nb==1.2.0
|
||||
myst-nb==1.3.0
|
||||
# via rocm-docs-core
|
||||
myst-parser==4.0.1
|
||||
# via myst-nb
|
||||
@@ -134,31 +132,30 @@ nest-asyncio==1.6.0
|
||||
packaging==25.0
|
||||
# via
|
||||
# ipykernel
|
||||
# pydata-sphinx-theme
|
||||
# sphinx
|
||||
parso==0.8.4
|
||||
parso==0.8.5
|
||||
# via jedi
|
||||
pexpect==4.9.0
|
||||
# via ipython
|
||||
platformdirs==4.3.8
|
||||
platformdirs==4.4.0
|
||||
# via jupyter-core
|
||||
prompt-toolkit==3.0.51
|
||||
prompt-toolkit==3.0.52
|
||||
# via ipython
|
||||
psutil==7.0.0
|
||||
psutil==7.1.0
|
||||
# via ipykernel
|
||||
ptyprocess==0.7.0
|
||||
# via pexpect
|
||||
pure-eval==0.2.3
|
||||
# via stack-data
|
||||
pycparser==2.22
|
||||
pycparser==2.23
|
||||
# via cffi
|
||||
pydata-sphinx-theme==0.15.4
|
||||
pydata-sphinx-theme==0.16.1
|
||||
# via
|
||||
# rocm-docs-core
|
||||
# sphinx-book-theme
|
||||
pygithub==2.6.1
|
||||
pygithub==2.8.1
|
||||
# via rocm-docs-core
|
||||
pygments==2.19.1
|
||||
pygments==2.19.2
|
||||
# via
|
||||
# accessible-pygments
|
||||
# ipython
|
||||
@@ -166,11 +163,11 @@ pygments==2.19.1
|
||||
# sphinx
|
||||
pyjwt[crypto]==2.10.1
|
||||
# via pygithub
|
||||
pynacl==1.5.0
|
||||
pynacl==1.6.0
|
||||
# via pygithub
|
||||
python-dateutil==2.9.0.post0
|
||||
# via jupyter-client
|
||||
pyyaml==6.0.2
|
||||
pyyaml==6.0.3
|
||||
# via
|
||||
# jupyter-cache
|
||||
# myst-nb
|
||||
@@ -178,7 +175,7 @@ pyyaml==6.0.2
|
||||
# rocm-docs-core
|
||||
# sphinx-external-toc
|
||||
# sphinxcontrib-datatemplates
|
||||
pyzmq==26.4.0
|
||||
pyzmq==27.1.0
|
||||
# via
|
||||
# ipykernel
|
||||
# jupyter-client
|
||||
@@ -186,13 +183,13 @@ referencing==0.36.2
|
||||
# via
|
||||
# jsonschema
|
||||
# jsonschema-specifications
|
||||
requests==2.32.4
|
||||
requests==2.32.5
|
||||
# via
|
||||
# pygithub
|
||||
# sphinx
|
||||
rocm-docs-core==1.20.1
|
||||
# via -r requirements.in
|
||||
rpds-py==0.25.1
|
||||
rocm-docs-core==1.26.0
|
||||
# via -r docs/sphinx/requirements.in
|
||||
rpds-py==0.27.1
|
||||
# via
|
||||
# jsonschema
|
||||
# referencing
|
||||
@@ -202,7 +199,7 @@ smmap==5.0.2
|
||||
# via gitdb
|
||||
snowballstemmer==3.0.1
|
||||
# via sphinx
|
||||
soupsieve==2.7
|
||||
soupsieve==2.8
|
||||
# via beautifulsoup4
|
||||
sphinx==8.1.3
|
||||
# via
|
||||
@@ -220,7 +217,7 @@ sphinx==8.1.3
|
||||
# sphinx-reredirects
|
||||
# sphinxcontrib-datatemplates
|
||||
# sphinxcontrib-runcmd
|
||||
sphinx-book-theme==1.1.4
|
||||
sphinx-book-theme==1.1.3
|
||||
# via rocm-docs-core
|
||||
sphinx-copybutton==0.5.2
|
||||
# via rocm-docs-core
|
||||
@@ -233,13 +230,13 @@ sphinx-last-updated-by-git==0.3.8
|
||||
sphinx-notfound-page==1.1.0
|
||||
# via rocm-docs-core
|
||||
sphinx-reredirects==0.1.6
|
||||
# via -r requirements.in
|
||||
sphinx-sitemap==2.8.0
|
||||
# via -r requirements.in
|
||||
# via -r docs/sphinx/requirements.in
|
||||
sphinx-sitemap==2.9.0
|
||||
# via -r docs/sphinx/requirements.in
|
||||
sphinxcontrib-applehelp==2.0.0
|
||||
# via sphinx
|
||||
sphinxcontrib-datatemplates==0.11.0
|
||||
# via -r requirements.in
|
||||
# via -r docs/sphinx/requirements.in
|
||||
sphinxcontrib-devhelp==2.0.0
|
||||
# via sphinx
|
||||
sphinxcontrib-htmlhelp==2.1.0
|
||||
@@ -252,7 +249,7 @@ sphinxcontrib-runcmd==0.2.0
|
||||
# via sphinxcontrib-datatemplates
|
||||
sphinxcontrib-serializinghtml==2.0.0
|
||||
# via sphinx
|
||||
sqlalchemy==2.0.41
|
||||
sqlalchemy==2.0.43
|
||||
# via jupyter-cache
|
||||
stack-data==0.6.3
|
||||
# via ipython
|
||||
@@ -260,13 +257,12 @@ tabulate==0.9.0
|
||||
# via jupyter-cache
|
||||
tomli==2.2.1
|
||||
# via sphinx
|
||||
tornado==6.5.1
|
||||
tornado==6.5.2
|
||||
# via
|
||||
# ipykernel
|
||||
# jupyter-client
|
||||
traitlets==5.14.3
|
||||
# via
|
||||
# comm
|
||||
# ipykernel
|
||||
# ipython
|
||||
# jupyter-client
|
||||
@@ -274,9 +270,10 @@ traitlets==5.14.3
|
||||
# matplotlib-inline
|
||||
# nbclient
|
||||
# nbformat
|
||||
typing-extensions==4.14.0
|
||||
typing-extensions==4.15.0
|
||||
# via
|
||||
# beautifulsoup4
|
||||
# cryptography
|
||||
# exceptiongroup
|
||||
# ipython
|
||||
# myst-nb
|
||||
@@ -288,9 +285,7 @@ urllib3==2.5.0
|
||||
# via
|
||||
# pygithub
|
||||
# requests
|
||||
wcwidth==0.2.13
|
||||
wcwidth==0.2.14
|
||||
# via prompt-toolkit
|
||||
wrapt==1.17.2
|
||||
# via deprecated
|
||||
zipp==3.23.0
|
||||
# via importlib-metadata
|
||||
|
||||
Reference in New Issue
Block a user