This change adds support for prefix caching to the v3 router. This
is broken up from the backend support to ease reviewing.
For now prefix caching is only enabled with `USE_PREFIX_CACHING=1`
in this case, the router will switch to `RadixAllocator`. This
allocator uses a radix trie to keep track of prefills that were
seen prior. If a new prefill is a prefix of a previously-seen
prefil, the router will send a request with `prefix_len>0`, which
can be used by the backend to decide to reuse KV blocks from the
cache, rather than recomputing them.
Even though backend support is not added in this PR, the backend
will still work with prefix caching enabled. The prefix lengths
are just ignored and not used.
This change adds support for FlashInfer. FlashInfer can be enabled using
`FLASH_INFER=1` and is currently only implemented in `FlashCausalLM`.
Since this functionality is currently only for testing, FlashInfer is
not installed anywhere yet.
The FlashInfer API is quite different from FlashAttention/vLLM in that
it requires more global bookkeeping:
* A wrapper class needs to be contstructed (which we just call *state*).
Since this is fairly expensive (due to pinned host memory allocation),
we only do this once in a FlashCausalLM instance or for each CUDA
Graph size.
* Each model forward call needs to be wrapped in `begin_forward` and
`end_forward`. This sets up data structures that can be reused for all
calls to attention for that forward call.
When calling attention, we need access to the state object. To avoid
passing an argument down the call chain (which would require changes to
all models), we use a context variable.
Each model forward call is wrapped using a context manager that does all
the bookkeeping for such a call:
* Set the context variable to the forward call's state.
* Call `begin_forward` on the state.
* Yield.
* Call `end_forward` on the state.
* Reset the context variable.
We cannot use a single shared global variable for this, since e.g. CUDA
Graphs of different sizes each have their own state.
* hotfix: fix xpu crash brought by code refine. torch.xpu rely on import ipex
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* reable gemma2 in xpu
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* fix in regression in ipex flashattention
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Wang, Yi A <yi.a.wang@intel.com>
* Update __init__.py
Fix issue with NoneType comparison for max_input_tokens and sliding_window
- Add default values for max_input_tokens and sliding_window to handle None cases.
- Ensure the comparison between max_input_tokens and sliding_window is handled correctly to prevent TypeError.
- This change addresses the error: TypeError: '<=' not supported between instances of 'int' and 'NoneType'.
* Update __init__.py
Handle NoneType in sliding_window comparison to fix TypeError in __init__.py by ensuring the comparison logic accounts for NoneType values, preventing errors and improving code robustness.
* fix: syntax/style tweak
---------
Co-authored-by: Praz <prazanth2006@gmail.com>
* add gptj modeling
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* fix: update docs for model addition
* fix: adjust syntax typo
* fix: adjust syntax typo again
---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Wang, Yi A <yi.a.wang@intel.com>
* fix: attempt forward on flash attn2 to check hardware support
* fix: warn window_size_left when using flash attn 1
* fix: prefer version check over test op and avoid window_size_left if not flash attn2
* fix: improve condtional and error message
* fix: update sliding window conditional
* fix: simplify changes and revert model changes
* fix: avoid changing conditional
* fix: typo tweak
- Always return the hidden states.
- Create the output tensor inside the `attention` and `paged_attention`
functions.
This removes the difference between how the output is handled between
attention (output parameter) and paged attention (return value). This
also removes the assumption that the attention implementation can
write to an output tensor (in preparation of FlashInfer).
The `GPTWeightLoader` was structured like this in pseudocode:
if marlin:
Set up tensors in a way that GPTQ-Marlin expects
else:
Set up tensors in a way that ExLlama/GPTQ/AWQ expect
However, the GPT-Marlin implementation details should really be in the
`marlin` module. So move the former part out to a separate
`GPTQMarlinWeightsLoader`.
* Fix GPTQ autotune data type to be compatible with Torch 2.4.0
* Update poetry lock file
* Fix small PaliGemma logprob differences after the torch update
* fix crash in multi-modal
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* update according to review comment
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* fix llava_next regression in latest main
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* Support passing head_dim through config
* Using `head_dim` as a fallback is necessary since it's a non standard
key in mistralConfig (as defined in transformers).
* Shorter diff.
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* Add support for repacking AWQ weights for GPTQ-Marlin
So far we couldn't support AWQ because virtually all AWQ models use
symmetric quantization, which GPTQ-Marlin did not suppors. GPTQ-Marlin
has recently added support AWQ repacking and AWQ asymmetric quantization
(zero_point=True).
This change updates all GPTQ-Marlin kernels from upstream and wires up
AWQ support. For now enabling AWQ using Marlin requires running TGI with
`--quantize gptq`.
* Enable Marlin for supported AWQ configurations by default
This makes the AWQ -> GPTQ repack test redundant, since we are now
testing this with the regular AWQ test.
Deepseek V2 is a MoE model from Deepseek. Relevant variations
compared to other models:
- Grouped top-K in expert selection.
- mscale in yarn is calculated using the `mscale` and `mscale_all_dim`
configuration options.
- `mscale_all_dim` is also used in scaling attention softmax.
- Permuting of the query/key representations before applying rotary
embeddings.
- Some projections cannot be sharded (`q_a_proj`, `kv_a_proj_with_mqa`).
So, we need weight loads that supports quantized weights. To this
end `{Weights,WeightLoader}.get_weight` was added.
- The query/key head dimensionality differs from that of the value,
so we need to pad during attention.
- Heads with size 192, needs an extension to our paged attention
fork and we need to ensure that the KV cache is allocated with the
correct size.
- Shared experts.
* Improve the handling of quantized weights
Handling of quantized weights was split between two mechanisms:
- For quantized checkpoints, we used the new weight loader
infrastructure.
- For quantization while loading (EETQ, FP8, bitsandbytes) we
instead relied on conditional in `get_linear`.
Weight loaders support context managers to selectively load
particular layers with different weight loaders, which is useful
for models like Idefics2 AWQ, which uses a quantized text model,
but unquantized vision and connector models. However, the context
manager would be overrided by `get_linear`, which string-checks
`quantizer`. Also, the context manager would not work with
EETQ, FP8, and bitsandbytes.
This change migrates all quantizers to the weight loader infrastructure.
This has several benefits:
- We can use context managers with all quantizers.
- All the implementation details move down to the quantizer layers,
`get_linear` does not need to know how to handle quantizer linear
layers.
- All quantizer weights are strongly typed, we don't pass around
raw tensors.
- We don't have to pass around the `quantizer` string everywhere.
* Exclude non-MLP layers when using FP8 quantization with Llama
* feat: simple mistral lora integration tests
* fix: include args in docker launcher
* fix: disable cuda graphs with lora and warn
* fix: adjust docs and precommit issues
* fix: re update docs
Packing of asymmetric quantization is broken, all (q)zeros values
of `0` get reset to `1`, resulting in a loss of accuracy. So instead
use symmetric quantization. To be able to distinguish models with
symmetric and asymmetric quantization, a new config tensor `gptq_sym` is
added. If this tensor is not present, we assume `sym=False`.
Use FP8 GPTQ-Marlin kernels to enable FP8 support on CUDA GPUs
with compute capability >=8.0 and <8.9.
Co-authored-by: Florian Zimmermeister <flozi00.fz@gmail.com>
Quantized weights were loaded in the `Weights` class, but this was
getting quite unwieldy, where every higher level method to load weights
was a long conditional to cover all the different quantizers.
This change moves loading of quantized weights out of the `Weights`
class. This is done by defining a simple `WeightsLoader` interface
that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`,
and `MarlinWeightsLoader`. These implementations are in the quantizers'
respective modules. The `Weights` class provides the low-level load
operations (such as loading tensors or sharded tensors), but delegates
loads that need quantizer-specific weight processing to a loader. The
loaders still use the low-level functionality provided by `Weights`.
I initially tried making a hierarchy where a class like `GPTQWeights`
would inherit from `Weights`. But it is not very flexible (e.g. does
not work well with the new weight storage mock used in tests) and
the implicit indirections made the code harder to follow.
* Refactor dead code.
* First working step.
* Remove a lot of duplicated code.
* More dead code.
* More cleanup.
* Fix Santacoder test.
* Fixing the simple tests.
* Fixing sharding.
* Fixes for VLM.
* Fixing santacoder (num_kv_heads hardcoded).
* Removing more dead code.
* Fixing `config.n_head`.
* Stopping earlier because of `<end_of_utterance>` in idefics2.
* Addresses comments.
* Removing the dead code.
* Fuse back mistral into FlashCausalLM.
* Finish removal.
* Fixing docs + causal_lm `batch_class`.
* Fixing docs + causal.lm.
* Add default to Gemma Causality.
* Default value for gemma/gemma2.
* Wrong default.
* Using flash decoding
Conditional flashdecoding.
Fix max_q.
Working kvcache
Working version with flash decoding.
Make it work for mistral.
Fix after rebase..
Less intrusive.
REvert changes in modeling.
Speedup flashdecoding.
HHachweew
Hack to make other models work.
Fixing non flash decoding llama path.
Router logic knows about page size.
Missing 2 models.
Missing cohere.
Fixing cohere flash decoding.
Revamped all this architecture.
Fix cohere.
Fixing falcon.
Enabling custom block size schedule.
Update router/src/infer.rs
Not sending preallocated output.
* Making it work on non flash decoding.
* Fix Cohere.
* Fix non decoding paths.
* Rebased.
* No need for cache_manager anymore.
* Update?
* "ipex" -> "cpu"
* These do not belong.
* Factoring cu_seqlen_qk for better abstracting over every model.
* Fixing non flash tests/imports.
* Changing return everywhere.
* Update mistral past.
* Fixing Mi{s,x}tral (non functional in Flash Decoding mode though).
* Fixup mistral clamping (had issues with cuda graphs).
* No need to recreate anything actually.
* refine get xpu free memory
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* enable qwen2 in xpu
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* enable gemma/gemma2/phi in intel platform
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
GPTQ-Marlin is currently the best-performing kernel for GPTQ models. So
let's use it by default if the kernels are installed, the GPU supports
it, and the kernels support the configuration.
For models generated by `text-generation-server quantize`, use
`sym=False`. This subcommand symmetric quantization since the beginning
and incorrectly reporting the model to be symmetric will use
GPTQ-Marlin (which does not support asymmetric quantization).
Before this change, the number of reserved image tokens was not the
same as the number of images. Fixes#2029.
While at it, also remove all the image token handling duplication
in `prepare_input`.
This change adds support for 2:4 sparsity when using Marlin
quantization. The 2:4 kernel is used when:
* The quantizer is `marlin`;
* the quantizer checkpoint format is `marlin_24`.
Fixes#2098.
When the AWQ quantizer was used with a layer that uses a bias,
the bias tensor was not correctly passed/used. Instead, the
value `true`/`1.0` was added to the linear transformation.
Correctly pass through the bias when it is not `None`.
Fixes#2106.
* feat: first draft load multiple lora
* feat: load weights within layer and refactor lora pass
* fix: refactor and reduce lora math
* feat: baseline impl single request multi lora support
* feat: prefer lorax implementation and port loading logic
* fix: prefer adapter_data and refactors
* feat: perfer loraxs custom punica kernels and add mlp loras
* fix: adjust batch for bgmv
* fix: adjust adapter_segments logic when in batch
* fix: refactor and move changes to v3 proto
* fix: pass model_id for all flash causal lms
* fix: pass model_id for all causal and seq2seq lms
* fix: add model_id to model test
* feat: add lora support to mistral and refactors
* feat: prefer model id in request
* fix: include rust code for adapter id
* feat: bump launcher and add new lora docs
* feat: support base model generation and refactors
* fix: rename doc to retry ci build
* feat: support if vlm models
* fix: add adapter_data param and avoid missing layers
* fix: add adapter_data param to phi and neox
* fix: update all models forwards to include adapter_data
* fix: add model_id to IdeficsCausalLM
* Update lora.md
Fixed a typo
* Update lora.md
Fixing spam image
* fix: add lora kernel to dockerfile, support running without kernels and refactors
* fix: avoid dockerfile conflict
* fix: refactors and adjust flash llama lora logic
* fix: skip llama test due to CI issue (temp)
* fix: skip llama test CI (temp) 2
* fix: revert skips and prefer updated ci token for tests
* fix: refactors and helpful comments
* fix: add noop in TensorParallelAdapterRowLinear too
* fix: refactor and move shard_lora_weights logic
* fix: exit early if no adapter_data
---------
Co-authored-by: Derek <datavistics@gmail.com>
* Removing IPEX_AVAIL.
Chose to unify CPU and XPU under `ipex`. Most code is exactly similar
except for a very few spots.
The biggest number of spots is the kv-cache layout and the flash_xxx.py
files.
Since those files should be removed soon and factored away, we should
not need them.
* Forgot a few places.
* Unrelated change.
* Fixing HF_TOKEN.
* HF_TOKEN
* add CPU tgi support
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* ipex distributed ops support
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Funtowicz Morgan <mfuntowicz@users.noreply.github.com>
* use xpu-smi to dump used memory
xpu use "ZE_AFFINITY_MASK" to control card, usage is like CUDA_VISIBLE_DEVICES
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* Update server/text_generation_server/utils/import_utils.py
Co-authored-by: Daniël de Kok <me@github.danieldk.eu>
---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Daniël de Kok <me@github.danieldk.eu>
For Phi-3-Small I need to shard a packed QKV bias tensor, for which
I implemented the `Weights.get_packed_sharded` method. However, this
method can also replace the `Weights._get_qweight` method and the
custom sharding code from `Weights.get_weights_col_packed`.
* Set maximum grpc message receive size to 2GiB
The previous default was 4MiB, which doesn't really work well for
multi-modal models.
* Update to Rust 1.79.0
* Fixup formatting to make PR pass
When a batch contained images if different sizes during prefill, the
server would fail (see e.g. #2056). Images were processed separately and
then concatenated. However, this can fail for images with different sizes.
Fix this by preprocessing all images in the batch together, so that the
image processor can ensure that all image tensors have compatible sizes.
Add support for GPTQ Marlin kernels
GPTQ Marlin extends the Marlin kernels to support common GPTQ
configurations:
- bits: 4 or 8
- groupsize: -1, 32, 64, or 128
- desc_act: true/false
Using the GPTQ Marlin kernels requires repacking the parameters in the
Marlin quantizer format.
The kernels were contributed by Neural Magic to VLLM. We vendor them
here for convenience.
drbh