[Misc][XPU] Upgrade to Pytorch 2.5 for xpu backend (#9823)
Signed-off-by: Kunshang Ji <kunshang.ji@intel.com> Signed-off-by: yan ma <yan.ma@intel.com> Co-authored-by: Kunshang Ji <kunshang.ji@intel.com>
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Yan Ma
GitHub
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d3859f1891
@ -30,9 +30,19 @@ COPY requirements-common.txt /workspace/vllm/requirements-common.txt
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RUN --mount=type=cache,target=/root/.cache/pip \
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pip install --no-cache-dir \
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--extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ \
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-r requirements-xpu.txt
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RUN git clone https://github.com/intel/pti-gpu && \
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cd pti-gpu/sdk && \
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git checkout 6c491f07a777ed872c2654ca9942f1d0dde0a082 && \
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mkdir build && \
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cd build && \
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cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=../cmake/toolchains/icpx_toolchain.cmake -DBUILD_TESTING=OFF .. && \
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make -j && \
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cmake --install . --config Release --prefix "/usr/local"
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ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/lib/"
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COPY . .
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ARG GIT_REPO_CHECK
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RUN --mount=type=bind,source=.git,target=.git \
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@ -8,9 +8,9 @@ packaging
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setuptools-scm>=8
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wheel
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jinja2
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# Following pkgs retrieved from https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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torch == 2.3.1+cxx11.abi
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intel-extension-for-pytorch == 2.3.110+xpu
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oneccl_bind_pt == 2.3.100+xpu
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torch @ https://intel-extension-for-pytorch.s3.us-east-1.amazonaws.com/ipex_dev/xpu/torch-2.5.0a0%2Bgite84e33f-cp310-cp310-linux_x86_64.whl
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intel-extension-for-pytorch @ https://intel-extension-for-pytorch.s3.us-east-1.amazonaws.com/ipex_dev/xpu/intel_extension_for_pytorch-2.5.10%2Bgit9d489a8-cp310-cp310-linux_x86_64.whl
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oneccl_bind_pt @ https://intel-extension-for-pytorch.s3.us-east-1.amazonaws.com/ipex_dev/xpu/oneccl_bind_pt-2.5.0%2Bxpu-cp310-cp310-linux_x86_64.whl
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triton-xpu == 3.0.0b1
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@ -74,20 +74,12 @@ class ipex_ops:
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assert kv_cache_dtype == "auto"
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num_heads = out.size(1)
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num_queries_per_tokens = num_heads // num_kv_heads
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head_mapping = torch.arange(
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0,
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num_kv_heads,
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device=query.device,
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dtype=torch.int32,
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).view(num_kv_heads,
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1).repeat_interleave(num_queries_per_tokens).flatten()
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# todo: ipex will refactor namespace
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torch.xpu.paged_attention_v1( # type: ignore
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ipex.llm.modules.PagedAttention.single_query_kv_attention(
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out,
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query.contiguous(),
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key_cache.view_as(value_cache),
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value_cache,
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head_mapping,
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num_queries_per_tokens,
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scale,
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block_tables,
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context_lens,
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@ -124,26 +116,15 @@ class ipex_ops:
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assert kv_cache_dtype == "auto"
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num_heads = out.size(1)
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num_queries_per_tokens = num_heads // num_kv_heads
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head_mapping = torch.arange(
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0,
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num_kv_heads,
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dtype=torch.int32,
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device=query.device,
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).view(num_kv_heads,
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1).repeat_interleave(num_queries_per_tokens).flatten()
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# todo: ipex will refactor namespace
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torch.xpu.paged_attention_v2( # type: ignore
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ipex.llm.modules.PagedAttention.single_query_kv_attention(
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out,
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exp_sum,
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max_logits,
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tmp_out,
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query.contiguous(),
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key_cache.view_as(value_cache),
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value_cache,
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head_mapping,
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num_queries_per_tokens,
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scale,
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block_tables,
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context_lens,
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scale,
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block_size,
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max_context_len,
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alibi_slopes,
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@ -202,6 +183,7 @@ class ipex_ops:
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is_causal: bool,
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return_softmax: bool,
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gen_: torch.Generator,
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logits_soft_cap: float,
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) -> None:
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ipex.llm.functional.varlen_attention(query.contiguous(),
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key.contiguous(),
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@ -210,7 +192,8 @@ class ipex_ops:
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max_seqlen_q, max_seqlen_k,
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pdropout, softmax_scale,
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zero_tensors, is_causal,
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return_softmax, gen_)
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return_softmax, gen_,
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logits_soft_cap)
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@staticmethod
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def reshape_and_cache(
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@ -119,8 +119,6 @@ class IpexAttnBackendImpl(AttentionImpl[IpexAttnMetadata]):
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if blocksparse_params is not None:
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raise ValueError(
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"IPEX backend does not support block-sparse attention.")
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if logits_soft_cap is not None:
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raise ValueError("IPEX backend does not support logits_soft_cap.")
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self.num_heads = num_heads
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self.head_size = head_size
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self.scale = float(scale)
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@ -135,6 +133,9 @@ class IpexAttnBackendImpl(AttentionImpl[IpexAttnMetadata]):
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self.num_queries_per_kv = self.num_heads // self.num_kv_heads
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self.need_mask = (self.alibi_slopes is not None
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or self.sliding_window is not None)
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if logits_soft_cap is None:
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logits_soft_cap = 0
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self.logits_soft_cap = logits_soft_cap
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supported_head_sizes = PagedAttention.get_supported_head_sizes()
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if head_size not in supported_head_sizes:
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@ -239,20 +240,23 @@ class IpexAttnBackendImpl(AttentionImpl[IpexAttnMetadata]):
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(num_tokens, self.num_heads, self.head_size),
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dtype=query.dtype,
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device=query.device)
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ipex_ops.varlen_attention(query,
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key,
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value,
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output,
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attn_metadata.seqlen_q,
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attn_metadata.seqlen_q,
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attn_metadata.max_seqlen,
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attn_metadata.max_seqlen,
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pdropout=0.0,
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softmax_scale=self.scale,
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zero_tensors=False,
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is_causal=True,
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return_softmax=False,
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gen_=None)
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ipex_ops.varlen_attention(
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query,
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key,
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value,
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output,
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attn_metadata.seqlen_q,
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attn_metadata.seqlen_q,
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attn_metadata.max_seqlen,
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attn_metadata.max_seqlen,
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pdropout=0.0,
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softmax_scale=self.scale,
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zero_tensors=False,
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is_causal=True,
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return_softmax=False,
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gen_=None,
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logits_soft_cap=self.logits_soft_cap,
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)
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else:
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# prefix-enabled attention
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raise RuntimeError(
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