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.
* fix nccl issue
* add note in dockerfile
* use v2.22.3 that also fixes @samsamoa's repro
* poetry actually can't handle the conflict between torch and nccl
* set LD_PRELOAD
* 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.
Add support for Phi-3-medium
The main difference between the medium and mini models is that medium
uses grouped query attention with a packed QKV matrix. This change adds
support for GQA with packed matrixes to `Weights.get_weights_col_packed`
and uses it for Phi-3. This also allows us to remove the custom
implementation of GQA from dbrx attention loading.
* update vllm commit & fix models using sliding window
* update
* update commit
* fix bug where tunableop is bound to cuda graph even when cuda graph are disabled
* enable tunableop by default
* fix sliding window
* address review
* dead code
* precise comment
* is it flaky?
The router will now send the input as chunks besides as a single
string. This change modifies the server to process chunked input
rather than strings. This also allows us to remove the image
extraction code from the server.
This change adds support for Marlin-quantized models. Marlin is an
FP16xINT4 matmul kernel, which provides good speedups decoding batches
of 16-32 tokens. It supports quantized models with symmetric
quantization, groupsize -1 or 128, and 4-bit.
Tested with:
- Llama 2
- Llama 3
- Phi 3
# What does this PR do?
Fix stray import.
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# What does this PR do?
Do not attempt to allocate ExLlamaV2 scratch buffers when there are no
ExLlama2 layers. Avoids a crash in warmup for models that cannot use
exllama when ExLlamaV2 is installed.
## Before submitting
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other checks if that's the case).
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# What does this PR do?
Fixes initial and subsequent installs (protection for folder creation
should only be for git commit, checking out correct commit should be on
both.
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# What does this PR do?
Making `make install` a much better sane default to start local dev
environments.
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# What does this PR do?
The GPTQ code path for column-packed packed tensors assumed that this is
always a QKV matrix. However, models (e.g. Phi-3) can also have
column-packed MLP up/gate matrices.
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- Refactor code to allow supporting multiple versions of the
generate.proto at the same time
- Add v3/generate.proto (ISO to generate.proto for now but allow for
future changes without impacting v2 backends)
- Add Schedule trait to abstract queuing and batching mechanisms that
will be different in the future
- Add SchedulerV2/V3 impl
# What does this PR do?
We were using the wrong parallelism in the up-projection.
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---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
# What does this PR do?
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Mostly straightforward, changes to existing code:
* Wrap quantizer parameters in a small wrapper to avoid passing
around untyped tuples and needing to repack them as a dict.
* Move scratch space computation to warmup, because we need the
maximum input sequence length to avoid allocating huge
scratch buffers that OOM.
This PR updates `load_attention` to prefer loading specific attention
based on the model type. Additionally there were two cases where
`TensorParallelColumnLinear.load_multi` was called and this reduces it
to a single path
# What does this PR do?
Fix GPTQ for models which do not have float16 at the default dtype
Before this change GPTQ models would not work if the model's default
data type is not `float16`. For example, Gemma GPTQ models would fail
because the default dtype of Gemma is `bfloat16`. There are two issues:
If the default `dtype` is not `float16`, the quantizer's `float16`
parameters get converted to that dtype. The kernels cannot deal
with non-`float16` types. The same applies to inputs of quantized ops.
This is resolved by setting the dtype of gptq/awq-quantized models to
`float16`.
Simpler version of #1951.
**Draft:** just testing...
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- The need for the slow tokenizer default stems from back
when llama 1 was introduced and all the flags where not
supported in `tokenizers`.
- Fixes#1891
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- Added a debug log for speculated ids (helps seeing in logs quality of
a speculator).
- Remove newlines from child process logs when re-emitting in non JSON
mode.
- Made standard level be closer to what's expected (only our binaries
level).
- Propagate that level correctly to the shard (was forced into INFO).
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---------
Co-authored-by: Daniël de Kok <me@github.danieldk.eu>
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Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
This PR access the path on the speculator similar to
`MLPSpeculatorHead.load` and `MedusaHeadV1.load`
these changes resolves this error locally when loading a `MedusaHeadV2`
```
TypeError: expected str, bytes or os.PathLike object, not dict
```
# What does this PR do?
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