diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml index cbb3392a..f9ccb842 100644 --- a/.github/workflows/tests.yaml +++ b/.github/workflows/tests.yaml @@ -48,6 +48,10 @@ jobs: uv venv source ./.venv/bin/activate make install-cpu + - name: Download locked kernels + run: | + source ./.venv/bin/activate + hf-kernels download server - name: Run server tests run: | source ./.venv/bin/activate diff --git a/Dockerfile b/Dockerfile index a963db2f..ead6c8c5 100644 --- a/Dockerfile +++ b/Dockerfile @@ -206,11 +206,13 @@ COPY proto proto COPY server server COPY server/Makefile server/Makefile ENV UV_SYSTEM_PYTHON=1 +ENV HF_KERNELS_CACHE=/kernels RUN cd server && \ pip install -U pip uv && \ uv sync --frozen --extra gen --extra attention --extra bnb --extra accelerate --extra compressed-tensors --extra marlin --extra moe --extra quantize --extra peft --extra outlines --no-install-project && \ . ./.venv/bin/activate && \ - make gen-server-raw + make gen-server-raw && \ + hf-kernels download . RUN cd server && \ uv sync --frozen --extra gen --extra attention --extra bnb --extra accelerate --extra compressed-tensors --extra marlin --extra moe --extra quantize --extra peft --extra outlines && \ diff --git a/flake.lock b/flake.lock index 3ad3d698..3a9d9c7c 100644 --- a/flake.lock +++ b/flake.lock @@ -853,11 +853,11 @@ ] }, "locked": { - "lastModified": 1737685583, - "narHash": "sha256-p+NVABRpGi+pT+xxf9HcLcFVxG6L+vEEy+NwzB9T0f8=", + "lastModified": 1738549608, + "narHash": "sha256-GdyT9QEUSx5k/n8kILuNy83vxxdyUfJ8jL5mMpQZWfw=", "owner": "oxalica", "repo": "rust-overlay", - "rev": "eb64cbcc8eee0fa87ebded92805280d2ec97415a", + "rev": "35c6f8c4352f995ecd53896200769f80a3e8f22d", "type": "github" }, "original": { @@ -978,11 +978,11 @@ "nixpkgs": "nixpkgs_6" }, "locked": { - "lastModified": 1738323634, - "narHash": "sha256-lKPzgEm7pEuQJVhacsxFHqg1MOtrUMZvr+9IuJzC5J4=", + "lastModified": 1738769628, + "narHash": "sha256-hgHf1mscFbH9XtT3dYtFQcxRfict9N+Vi6QSW1c+FjU=", "owner": "huggingface", "repo": "text-generation-inference-nix", - "rev": "eb5fede2756f544f75e01f55a4097f9c9a8c5005", + "rev": "9a5a58219dead9704d83d9d32f105b6b90bd31f2", "type": "github" }, "original": { diff --git a/nix/impure-shell.nix b/nix/impure-shell.nix index aebdff84..9b8d7e35 100644 --- a/nix/impure-shell.nix +++ b/nix/impure-shell.nix @@ -90,7 +90,7 @@ mkShell { postVenvCreation = '' unset SOURCE_DATE_EPOCH - ( cd server ; python -m pip install --no-dependencies -e . ) + ( cd server ; python -m pip install --no-build-isolation --no-dependencies -e . ) ( cd clients/python ; python -m pip install --no-dependencies -e . ) ''; diff --git a/nix/server.nix b/nix/server.nix index 237102a8..b638449b 100644 --- a/nix/server.nix +++ b/nix/server.nix @@ -3,7 +3,6 @@ buildPythonPackage, poetry-core, mypy-protobuf, - attention-kernels, awq-inference-engine, causal-conv1d, compressed-tensors, @@ -19,22 +18,24 @@ grpcio-reflection, grpcio-status, grpcio-tools, + hf-kernels, hf-transfer, loguru, mamba-ssm, - marlin-kernels, - moe-kernels, + moe, opentelemetry-api, opentelemetry-exporter-otlp, opentelemetry-instrumentation-grpc, opentelemetry-semantic-conventions, outlines, + paged-attention, peft, pillow, prometheus-client, punica-kernels, py-cpuinfo, pydantic, + quantization, safetensors, tokenizers, torch, @@ -78,7 +79,6 @@ buildPythonPackage { pythonRemoveDeps = [ "scipy" ]; dependencies = [ - attention-kernels awq-inference-engine eetq causal-conv1d @@ -93,22 +93,24 @@ buildPythonPackage { grpcio-reflection grpcio-status grpcio-tools + hf-kernels hf-transfer loguru mamba-ssm - marlin-kernels - moe-kernels + moe opentelemetry-api opentelemetry-exporter-otlp opentelemetry-instrumentation-grpc opentelemetry-semantic-conventions outlines + paged-attention peft pillow prometheus-client punica-kernels py-cpuinfo pydantic + quantization safetensors sentencepiece tokenizers diff --git a/server/hf-kernels.lock b/server/hf-kernels.lock new file mode 100644 index 00000000..43f7f17d 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"hf-transfer>=0.1.8", "loguru>=0.7.3", "numpy>=1.26,<3", @@ -33,6 +34,15 @@ dependencies = [ "transformers>=4.48.0" ] +[build-system] +requires = ["hf-kernels>=0.1.2", "setuptools"] +build-backend = "setuptools.build_meta" + +[tool.kernels.dependencies] +"kernels-community/paged-attention" = ">=0.0.2" +"kernels-community/moe" = ">=0.1.1" +"kernels-community/quantization" = ">=0.0.3" + [project.scripts] text-generation-server = "text_generation_server.cli:app" @@ -60,24 +70,11 @@ quantize = [ "texttable>=1.6.7,<2", "datasets>=2.21,<3", ] -moe = [ "moe-kernels" ] -attention = [ "attention-kernels" ] -marlin = [ "marlin-kernels" ] gen = [ "grpcio-tools>=1.69.0", "mypy-protobuf>=3.6.0", ] -[tool.uv.sources] -attention-kernels.url = "https://github.com/danieldk/attention-kernels/releases/download/v0.2.0.post2/attention_kernels-0.2.0.post2+cu123torch2.5-cp39-abi3-linux_x86_64.whl" -marlin-kernels = [ - { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.5-cp39-cp39-linux_x86_64.whl", marker = "python_version == '3.9'" }, - { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.5-cp310-cp310-linux_x86_64.whl", marker = "python_version == '3.10'" }, - { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.5-cp311-cp311-linux_x86_64.whl", marker = "python_version == '3.11'" }, - { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.5-cp312-cp312-linux_x86_64.whl", marker = "python_version == '3.12'" }, -] -moe-kernels.url = "https://github.com/danieldk/moe-kernels/releases/download/v0.8.2/moe_kernels-0.8.2+cu123torch2.5-cp39-abi3-linux_x86_64.whl" - [tool.pytest.ini_options] markers = ["private: marks tests as requiring an admin hf token (deselect with '-m \"not private\"')"] diff --git a/server/text_generation_server/layers/attention/cuda.py b/server/text_generation_server/layers/attention/cuda.py index 6b77f591..4f25cc19 100644 --- a/server/text_generation_server/layers/attention/cuda.py +++ b/server/text_generation_server/layers/attention/cuda.py @@ -1,6 +1,7 @@ import torch from text_generation_server.layers.attention.kv_cache import KVCache, KVScales from text_generation_server.utils.import_utils import SYSTEM +from text_generation_server.utils.kernels import load_kernel from text_generation_server.models.globals import ( ATTENTION, BLOCK_SIZE, @@ -13,6 +14,18 @@ major, minor = torch.cuda.get_device_capability() is_sm75 = major == 7 and minor == 5 _PARTITION_SIZE = 512 +if SYSTEM == "cuda": + try: + paged_attention_kernels = load_kernel( + module="paged_attention", repo_id="kernels-community/paged-attention" + ) + except Exception as e: + raise ImportError( + f"Could not import attention kernels. Make sure your installation is correct. Complete error: {e}" + ) +else: + paged_attention_kernels = None + def paged_attention( query: torch.Tensor, @@ -107,7 +120,6 @@ def paged_attention( if softcap is not None: raise RuntimeError("Paged attention doesn't support softcapping") input_lengths = seqlen.input_lengths + seqlen.cache_lengths - import attention_kernels out = torch.empty_like(query) @@ -117,7 +129,7 @@ def paged_attention( max_num_partitions == 1 or num_seqs * num_heads > 512 ) if use_v1: - attention_kernels.paged_attention_v1( + paged_attention_kernels.paged_attention_v1( out, query, kv_cache.key, @@ -130,8 +142,8 @@ def paged_attention( max_s, None, kv_cache_dtype, - kv_scales.key_scale_cpu, - kv_scales.value_scale_cpu, + torch.tensor(kv_scales.key_scale_cpu if can_scale else 1.0), + torch.tensor(kv_scales.value_scale_cpu if can_scale else 1.0), ) else: # Run PagedAttention V2. @@ -148,7 +160,7 @@ def paged_attention( ) max_logits = torch.empty_like(exp_sums) - attention_kernels.paged_attention_v2( + paged_attention_kernels.paged_attention_v2( out, exp_sums, max_logits, @@ -164,8 +176,8 @@ def paged_attention( max_s, None, kv_cache_dtype, - kv_scales.key_scale_cpu, - kv_scales.value_scale_cpu, + torch.tensor(kv_scales.key_scale_cpu if can_scale else 1.0), + torch.tensor(kv_scales.value_scale_cpu if can_scale else 1.0), ) return out diff --git a/server/text_generation_server/layers/attention/kv_cache.py b/server/text_generation_server/layers/attention/kv_cache.py index bca7959b..aaf4d2b2 100644 --- a/server/text_generation_server/layers/attention/kv_cache.py +++ b/server/text_generation_server/layers/attention/kv_cache.py @@ -7,9 +7,22 @@ import torch from text_generation_server.layers.fp8 import fp8_quantize from text_generation_server.models.globals import ATTENTION, BLOCK_SIZE from text_generation_server.utils.import_utils import SYSTEM +from text_generation_server.utils.kernels import load_kernel from text_generation_server.utils.log import log_once from text_generation_server.utils.weights import Weights +if SYSTEM == "cuda": + try: + paged_attention = load_kernel( + module="paged_attention", repo_id="kernels-community/paged-attention" + ) + except Exception as e: + raise ImportError( + f"Could not import attention kernels. Make sure your installation is correct. Complete error: {e}" + ) +else: + paged_attention = None + @dataclass class KVScales: @@ -119,7 +132,7 @@ class KVCache: if kv_scales.key_scale_cpu == 1.0 and kv_scales.value_scale_cpu == 1.0: return False elif self.dtype == torch.float8_e4m3fn and ( - (ATTENTION == "flashinfer" and SYSTEM == "cuda") + (ATTENTION in ("paged", "flashinfer") and SYSTEM == "cuda") or (ATTENTION == "paged" and SYSTEM == "rocm") ): log_once(logger.info, "Using FP8 KV cache scales") @@ -220,19 +233,19 @@ def paged_reshape_and_cache( ): if SYSTEM == "cuda": - try: - import attention_kernels - except Exception as e: - raise ImportError( - f"Could not import attention_kernels. Make sure your installation is correct. Complete error: {e}" - ) - kv_cache_dtype = "auto" if key_cache.dtype == torch.float8_e4m3fn: kv_cache_dtype = "fp8" - attention_kernels.reshape_and_cache( - key, value, key_cache, value_cache, slots, kv_cache_dtype, k_scale, v_scale + paged_attention.reshape_and_cache( + key, + value, + key_cache, + value_cache, + slots, + kv_cache_dtype, + torch.tensor(k_scale), + torch.tensor(v_scale), ) elif SYSTEM == "rocm": try: diff --git a/server/text_generation_server/layers/compressed_tensors/w8a8_int.py b/server/text_generation_server/layers/compressed_tensors/w8a8_int.py index fc6d81e4..b66057ec 100644 --- a/server/text_generation_server/layers/compressed_tensors/w8a8_int.py +++ b/server/text_generation_server/layers/compressed_tensors/w8a8_int.py @@ -6,13 +6,17 @@ import torch from compressed_tensors.quantization import QuantizationArgs, QuantizationType from text_generation_server.layers.fp8 import _load_scalar_or_matrix_scale +from text_generation_server.utils.import_utils import SYSTEM +from text_generation_server.utils.kernels import load_kernel from text_generation_server.utils.log import log_once from text_generation_server.utils.weights import Weight, Weights, WeightsLoader -try: - import marlin_kernels -except ImportError: - marlin_kernels = None +if SYSTEM == "cuda": + quantization = load_kernel( + module="quantization", repo_id="kernels-community/quantization" + ) +else: + quantization = None class W8A8IntLoader(WeightsLoader): @@ -159,8 +163,8 @@ class Int8Weight(Weight): def get_linear(self, bias: torch.Tensor): if self.weight_scale is None: - assert marlin_kernels is not None - qweight, weight_scale, _ = marlin_kernels.scaled_int8_quant(self.weight) + assert quantization is not None + qweight, weight_scale, _ = quantization.scaled_int8_quant(self.weight) return W8A8IntLinear( bias=bias, input_symmetric=self.input_symmetric, @@ -204,9 +208,9 @@ class W8A8IntLinear(torch.nn.Module): ) def forward(self, input: torch.Tensor) -> torch.Tensor: - assert marlin_kernels is not None + assert quantization is not None - qinput, input_scale, input_zero_point = marlin_kernels.scaled_int8_quant( + qinput, input_scale, input_zero_point = quantization.scaled_int8_quant( input=input, scale=None, azp=None, @@ -214,7 +218,7 @@ class W8A8IntLinear(torch.nn.Module): ) if self.input_symmetric: - return marlin_kernels.cutlass_scaled_mm( + return quantization.cutlass_scaled_mm( a=qinput, b=self.weight, scale_a=input_scale, @@ -229,7 +233,7 @@ class W8A8IntLinear(torch.nn.Module): and (self.input_symmetric or input_zero_point is not None) ) - return marlin_kernels.cutlass_scaled_mm_azp( + return quantization.cutlass_scaled_mm_azp( a=qinput, b=self.weight, scale_a=input_scale, diff --git a/server/text_generation_server/layers/fp8.py b/server/text_generation_server/layers/fp8.py index ae20235d..04689ed9 100644 --- a/server/text_generation_server/layers/fp8.py +++ b/server/text_generation_server/layers/fp8.py @@ -6,6 +6,7 @@ import torch from loguru import logger from text_generation_server.utils.import_utils import SYSTEM +from text_generation_server.utils.kernels import load_kernel from text_generation_server.utils.weights import ( Weight, WeightsLoader, @@ -14,10 +15,12 @@ from text_generation_server.utils.weights import ( ) from text_generation_server.utils.log import log_once -try: - import marlin_kernels -except ImportError: - marlin_kernels = None +if SYSTEM == "cuda": + quantization = load_kernel( + module="quantization", repo_id="kernels-community/quantization" + ) +else: + quantization = None try: from moe_kernels.fp8_utils import w8a8_block_fp8_matmul, per_token_group_quant_fp8 @@ -29,9 +32,9 @@ quant_dtype: torch.dtype = ( torch.float8_e4m3fnuz if SYSTEM == "rocm" else torch.float8_e4m3fn ) -if SYSTEM == "cuda" and marlin_kernels is not None: +if SYSTEM == "cuda" and quantization is not None: major, minor = torch.cuda.get_device_capability() - CUTLASS_FP8_AVAILABLE = marlin_kernels.cutlass_scaled_mm_supports_fp8( + CUTLASS_FP8_AVAILABLE = quantization.cutlass_scaled_mm_supports_fp8( major * 10 + minor ) else: @@ -143,11 +146,10 @@ def fp8_quantize( argument, it must also be a reciprocal (so that scales from an FP8 checkpoint can be used without modification). """ - if marlin_kernels is not None: + if quantization is not None: shape = weight.shape - qweight, scale = marlin_kernels.scaled_fp8_quant( + qweight, scale = quantization.scaled_fp8_quant( weight.reshape(-1, shape[-1]), - dtype=quant_dtype, scale=scale, scale_ub=scale_upper_bound, # TODO: don't do this when we have to use the Torch kernel. @@ -527,7 +529,7 @@ class Fp8Linear(torch.nn.Module): qinput, scale = fp8_quantize( input, scale_upper_bound=self.scale_upper_bound, scalar=False ) - return marlin_kernels.cutlass_scaled_mm( + return quantization.cutlass_scaled_mm( qinput, self.qweight.t(), scale, self.scale, input.dtype, self.bias ) diff --git a/server/text_generation_server/layers/marlin/fp8.py b/server/text_generation_server/layers/marlin/fp8.py index e07b9fc6..10751a05 100644 --- a/server/text_generation_server/layers/marlin/fp8.py +++ b/server/text_generation_server/layers/marlin/fp8.py @@ -8,11 +8,15 @@ from text_generation_server.layers.marlin.util import ( _check_marlin_kernels, permute_scales, ) +from text_generation_server.utils.import_utils import SYSTEM +from text_generation_server.utils.kernels import load_kernel -try: - import marlin_kernels -except ImportError: - marlin_kernels = None +if SYSTEM == "cuda": + quantization = load_kernel( + module="quantization", repo_id="kernels-community/quantization" + ) +else: + quantization = None MARLIN_TILE_SIZE = 16 @@ -32,7 +36,7 @@ class GPTQMarlinFP8Linear(nn.Module): super().__init__() _check_marlin_kernels() - assert marlin_kernels is not None + assert quantization is not None scales = scales.unsqueeze(0) if scales.shape[1] == 1: @@ -69,10 +73,10 @@ class GPTQMarlinFP8Linear(nn.Module): return cls(qweight=weight, scales=scale.to(dtype), bias=bias) def forward(self, A: torch.Tensor) -> torch.Tensor: - assert marlin_kernels is not None + assert quantization is not None A_flat = A.view(-1, A.shape[-1]) - C = marlin_kernels.fp8_marlin_gemm( + C = quantization.fp8_marlin_gemm( A_flat, self.qweight, self.scales, @@ -134,7 +138,7 @@ def repack_fp8_for_marlin(weight: torch.Tensor, scales: torch.Tensor): qweight = pack_fp8_as_int32(weight.t()) perm = torch.empty(0, dtype=torch.int, device=qweight.device) - repacked = marlin_kernels.gptq_marlin_repack( + repacked = quantization.gptq_marlin_repack( qweight, perm, in_features, out_features, 8 ) diff --git a/server/text_generation_server/layers/marlin/gptq.py b/server/text_generation_server/layers/marlin/gptq.py index 5c1bb549..e85c8333 100644 --- a/server/text_generation_server/layers/marlin/gptq.py +++ b/server/text_generation_server/layers/marlin/gptq.py @@ -12,13 +12,17 @@ from text_generation_server.layers.marlin.util import ( unpack_cols, ) from text_generation_server.utils.import_utils import SYSTEM +from text_generation_server.utils.kernels import load_kernel from text_generation_server.utils.log import log_once from text_generation_server.utils.weights import Weight, Weights, WeightsLoader -try: - import marlin_kernels -except ImportError: - marlin_kernels = None +if SYSTEM == "cuda": + quantization = load_kernel( + module="quantization", repo_id="kernels-community/quantization" + ) +else: + quantization = None + try: major, _minor = torch.cuda.get_device_capability() @@ -37,7 +41,7 @@ def can_use_gptq_marlin( ) -> bool: return ( SYSTEM == "cuda" - and marlin_kernels is not None + and quantization is not None and has_sm_8_0 and quantize in {"awq", "gptq"} and quant_method in {"awq", "gptq"} @@ -287,7 +291,7 @@ def repack_gptq_for_marlin( ) -> GPTQMarlinWeight: """Convert GPTQ weights to a layout that's compatible with GPTQ-Marlin kernels.""" _check_marlin_kernels() - assert marlin_kernels is not None + assert quantization is not None if bits not in GPTQ_MARLIN_BITS: supported_bits = ", ".join(str(b) for b in GPTQ_MARLIN_BITS) @@ -330,7 +334,7 @@ def repack_gptq_for_marlin( g_idx = torch.empty(0, dtype=torch.int, device=qweight.device) if quant_method == "awq": - repacked = marlin_kernels.awq_marlin_repack( + repacked = quantization.awq_marlin_repack( qweight, in_features, out_features, bits ) if qzeros is not None: @@ -342,7 +346,7 @@ def repack_gptq_for_marlin( ) else: - repacked = marlin_kernels.gptq_marlin_repack( + repacked = quantization.gptq_marlin_repack( qweight, perm, in_features, out_features, bits ) @@ -379,13 +383,26 @@ class GPTQMarlinLinear(nn.Module): super().__init__() _check_marlin_kernels() - assert marlin_kernels is not None + assert quantization is not None in_features = weight.qweight.shape[0] * MARLIN_TILE_SIZE out_features = weight.scales.shape[1] _check_valid_shape(in_features=in_features, out_features=out_features) - self.bits = weight.bits + if weight.bits not in (4, 8): + raise ValueError("GPTQMarlinLinear only supports 4 and 8-bit quantization") + + if weight.qzeros.numel() > 0: + if weight.bits == 4: + self.quant_type = quantization.scalar_types.uint4 + else: + self.quant_type = quantization.scalar_types.uint8 + else: + if weight.bits == 4: + self.quant_type = quantization.scalar_types.uint4b8 + else: + self.quant_type = quantization.scalar_types.uint8b128 + self.is_full_k = weight.is_full_k self.qweight = weight.qweight @@ -403,10 +420,10 @@ class GPTQMarlinLinear(nn.Module): ) def forward(self, A: torch.Tensor) -> torch.Tensor: - assert marlin_kernels is not None + assert quantization is not None A_flat = A.view(-1, A.shape[-1]) - C = marlin_kernels.gptq_marlin_gemm( + C = quantization.gptq_marlin_gemm( A_flat, self.qweight, self.scales, @@ -414,7 +431,7 @@ class GPTQMarlinLinear(nn.Module): self.g_idx, self.perm, self.workspace, - self.bits, + self.quant_type, A_flat.shape[0], self.scales.shape[1], A_flat.shape[1], diff --git a/server/text_generation_server/layers/marlin/marlin.py b/server/text_generation_server/layers/marlin/marlin.py index 1c80e31e..48aedc72 100644 --- a/server/text_generation_server/layers/marlin/marlin.py +++ b/server/text_generation_server/layers/marlin/marlin.py @@ -3,13 +3,18 @@ from typing import List, Optional, Union import torch import torch.nn as nn + from text_generation_server.layers.marlin.util import _check_marlin_kernels +from text_generation_server.utils.import_utils import SYSTEM +from text_generation_server.utils.kernels import load_kernel from text_generation_server.utils.weights import Weight, Weights, WeightsLoader -try: - import marlin_kernels -except ImportError: - marlin_kernels = None +if SYSTEM == "cuda": + quantization = load_kernel( + module="quantization", repo_id="kernels-community/quantization" + ) +else: + quantization = None class MarlinWeightsLoader(WeightsLoader): @@ -187,7 +192,7 @@ class MarlinLinear(nn.Module): super().__init__() _check_marlin_kernels() - assert marlin_kernels is not None + assert quantization is not None in_features = weight.B.shape[0] * MARLIN_TILE_SIZE out_features = weight.s.shape[1] @@ -216,9 +221,9 @@ class MarlinLinear(nn.Module): ) def forward(self, A: torch.Tensor) -> torch.Tensor: - assert marlin_kernels is not None + assert quantization is not None - C = marlin_kernels.marlin_gemm( + C = quantization.marlin_gemm( A.view(-1, A.shape[-1]), self.B, self.s, @@ -277,7 +282,7 @@ class GPTQMarlin24Linear(nn.Module): super().__init__() _check_marlin_kernels() - assert marlin_kernels is not None + assert quantization is not None if weight.bits not in GPTQ_MARLIN_24_SUPPORTED_NUM_BITS: supported_bits = ", ".join( @@ -303,8 +308,11 @@ class GPTQMarlin24Linear(nn.Module): f"Group size {groupsize} is not supported, must be one of: {supported_sizes}" ) - self.bits = weight.bits - weights_per_int32 = 32 // self.bits + if weight.bits == 4: + self.quant_type = quantization.scalar_types.uint4b8 + else: + self.quant_type = quantization.scalar_types.uint8b128 + weights_per_int32 = 32 // weight.bits assert ( out_features % GPTQ_MARLIN_24_MIN_THREAD_N == 0 @@ -336,15 +344,15 @@ class GPTQMarlin24Linear(nn.Module): ) def forward(self, A: torch.Tensor) -> torch.Tensor: - assert marlin_kernels is not None + assert quantization is not None - C = marlin_kernels.gptq_marlin_24_gemm( + C = quantization.gptq_marlin_24_gemm( A.view(-1, A.shape[-1]), self.weight_packed, self.meta, self.scale_packed, self.workspace, - self.bits, + self.quant_type, A.shape[0], self.scale_packed.shape[1], A.shape[1], diff --git a/server/text_generation_server/layers/marlin/util.py b/server/text_generation_server/layers/marlin/util.py index 250d1714..0c5d715f 100644 --- a/server/text_generation_server/layers/marlin/util.py +++ b/server/text_generation_server/layers/marlin/util.py @@ -4,11 +4,14 @@ from typing import List, Tuple import numpy import torch from text_generation_server.utils.import_utils import SYSTEM +from text_generation_server.utils.kernels import load_kernel -try: - import marlin_kernels -except ImportError: - marlin_kernels = None +if SYSTEM == "cuda": + quantization = load_kernel( + module="quantization", repo_id="kernels-community/quantization" + ) +else: + quantization = None try: major, _minor = torch.cuda.get_device_capability() @@ -23,7 +26,7 @@ def _check_marlin_kernels(): "Using quantized Marlin models requires a GPU with CUDA capability 8.0 or later." ) - if marlin_kernels is None: + if quantization is None: raise NotImplementedError( "marlin is not installed, install it with: pip install server/marlin" ) diff --git a/server/text_generation_server/layers/moe/__init__.py b/server/text_generation_server/layers/moe/__init__.py index 23d0d38c..e148f165 100644 --- a/server/text_generation_server/layers/moe/__init__.py +++ b/server/text_generation_server/layers/moe/__init__.py @@ -18,6 +18,7 @@ from text_generation_server.layers.moe.gptq_marlin import ( from text_generation_server.layers.moe.unquantized import UnquantizedSparseMoELayer from text_generation_server.layers.moe.fp8 import FP8SparseMoELayer from text_generation_server.utils.import_utils import SYSTEM +from text_generation_server.utils.kernels import load_kernel from text_generation_server.utils.log import log_once from text_generation_server.utils.weights import ( DefaultWeightsLoader, @@ -27,6 +28,10 @@ from text_generation_server.utils.weights import ( if SYSTEM == "ipex": from .fused_moe_ipex import fused_topk, grouped_topk +elif SYSTEM == "cuda": + moe_kernels = load_kernel(module="moe", repo_id="kernels-community/moe") + fused_topk = moe_kernels.fused_topk + grouped_topk = moe_kernels.grouped_topk else: from moe_kernels.fused_moe import fused_topk, grouped_topk diff --git a/server/text_generation_server/layers/moe/fp8.py b/server/text_generation_server/layers/moe/fp8.py index 3016c8a2..071b2abe 100644 --- a/server/text_generation_server/layers/moe/fp8.py +++ b/server/text_generation_server/layers/moe/fp8.py @@ -12,7 +12,7 @@ from text_generation_server.layers.fp8 import ( ) try: - from moe_kernels.fused_moe import fused_moe + from .unquantized import fused_moe except Exception: fused_moe = None diff --git a/server/text_generation_server/layers/moe/gptq_marlin.py b/server/text_generation_server/layers/moe/gptq_marlin.py index 014a90dc..75c076ab 100644 --- a/server/text_generation_server/layers/moe/gptq_marlin.py +++ b/server/text_generation_server/layers/moe/gptq_marlin.py @@ -1,10 +1,12 @@ from dataclasses import dataclass -from typing import List, Optional +from typing import Callable, List, Optional import torch import torch.nn as nn +from text_generation_server.layers import moe from text_generation_server.utils.import_utils import SYSTEM +from text_generation_server.utils.kernels import load_kernel from text_generation_server.utils.weights import Weights from text_generation_server.layers.marlin.gptq import ( GPTQMarlinWeight, @@ -12,9 +14,9 @@ from text_generation_server.layers.marlin.gptq import ( ) if SYSTEM == "cuda": - from moe_kernels.fused_marlin_moe import fused_marlin_moe + moe_kernels = load_kernel(module="moe", repo_id="kernels-community/moe") else: - fused_marlin_moe = None + moe_kernels = None try: @@ -32,7 +34,7 @@ def can_use_marlin_moe_gemm( ): return ( SYSTEM == "cuda" - and fused_marlin_moe is not None + and moe is not None and has_sm_8_0 and quantize in {"awq", "gptq"} and quant_method in {"awq", "gptq"} @@ -230,3 +232,111 @@ def _pack_weight( moe_weight.perm[expert] = weight.perm return moe_weight + + +def fused_marlin_moe( + *, + hidden_states: torch.Tensor, + w1: torch.Tensor, + w2: torch.Tensor, + w1_scale: Optional[torch.Tensor] = None, + w2_scale: Optional[torch.Tensor] = None, + gating_output: torch.Tensor, + g_idx1: torch.Tensor, + g_idx2: torch.Tensor, + sort_indices1: torch.Tensor, + sort_indices2: torch.Tensor, + w1_zeros: Optional[torch.Tensor] = None, + w2_zeros: Optional[torch.Tensor] = None, + is_k_full: bool, + topk: int, + renormalize: bool, + num_bits: int = 8, + use_grouped_topk: bool = False, + num_expert_group: Optional[int] = None, + custom_routing_function: Optional[Callable] = None, + topk_group: Optional[int] = None, +) -> torch.Tensor: + """ + This function computes a Mixture of Experts (MoE) layer using two sets of + weights, w1 and w2, and top-k gating mechanism. + + Parameters: + - hidden_states (torch.Tensor): The input tensor to the MoE layer. + - w1 (torch.Tensor): The first set of expert weights. + - w2 (torch.Tensor): The second set of expert weights. + - w1_scale (Optional[torch.Tensor]): Optional scale to be used for + w1. + - w2_scale (Optional[torch.Tensor]): Optional scale to be used for + w2. + - gating_output (torch.Tensor): The output of the gating operation + (before softmax). + - g_idx1 (torch.Tensor): The first set of act_order indices. + - g_idx2 (torch.Tensor): The second set of act_order indices. + - sort_indices1 (torch.Tensor): The first act_order input permutation. + - sort_indices2 (torch.Tensor): The second act_order input permutation. + - w1_zeros (Optional[torch.Tensor]): Optional zero points to be used for w1. + - w2_zeros (Optional[torch.Tensor]): Optional zero points to be used for w2. + - renormalize (bool): If True, renormalize the top-k weights to sum to 1. + - num_bits (bool): The number of bits in expert weights quantization. + + Returns: + - torch.Tensor: The output tensor after applying the MoE layer. + """ + # Check constraints. + assert hidden_states.shape[0] == gating_output.shape[0], "Number of tokens mismatch" + assert hidden_states.shape[1] == w1.shape[1] * 16, "Hidden size mismatch w1" + assert hidden_states.shape[1] == w2.shape[2] // ( + num_bits // 2 + ), "Hidden size mismatch w2" + assert gating_output.shape[1] == w1.shape[0], "Number of experts mismatch" + assert hidden_states.is_contiguous(), "Hidden_states must be contiguous" + assert w1.is_contiguous(), "Expert weights1 must be contiguous" + assert w2.is_contiguous(), "Expert weights2 must be contiguous" + assert hidden_states.dtype == torch.float16 + assert num_bits in [4, 8] + + # DeekSeekv2 uses grouped_top_k + if use_grouped_topk: + assert topk_group is not None + assert num_expert_group is not None + topk_weights, topk_ids = moe_kernels.grouped_topk( + hidden_states=hidden_states, + gating_output=gating_output, + topk=topk, + renormalize=renormalize, + num_expert_group=num_expert_group, + topk_group=topk_group, + ) + elif custom_routing_function is None: + topk_weights, topk_ids = moe_kernels.fused_topk( + hidden_states=hidden_states, + gating_output=gating_output, + topk=topk, + renormalize=renormalize, + ) + else: + topk_weights, topk_ids = custom_routing_function( + hidden_states=hidden_states, + gating_output=gating_output, + topk=topk, + renormalize=renormalize, + ) + return moe_kernels.fused_marlin_moe( + hidden_states=hidden_states, + w1=w1, + w2=w2, + w1_scale=w1_scale, + w2_scale=w2_scale, + gating_output=gating_output, + topk_weights=topk_weights, + topk_ids=topk_ids, + g_idx1=g_idx1, + g_idx2=g_idx2, + sort_indices1=sort_indices1, + sort_indices2=sort_indices2, + w1_zeros=w1_zeros, + w2_zeros=w2_zeros, + num_bits=num_bits, + is_k_full=is_k_full, + ) diff --git a/server/text_generation_server/layers/moe/unquantized.py b/server/text_generation_server/layers/moe/unquantized.py index 9277384a..77214286 100644 --- a/server/text_generation_server/layers/moe/unquantized.py +++ b/server/text_generation_server/layers/moe/unquantized.py @@ -1,15 +1,18 @@ -from typing import Optional +from typing import Callable, List, Optional import torch import torch.nn as nn from text_generation_server.utils.import_utils import SYSTEM +from text_generation_server.utils.kernels import load_kernel from text_generation_server.utils.weights import UnquantizedWeight, Weights if SYSTEM == "ipex": from intel_extension_for_pytorch.llm.modules import GatedMLPMOE +elif SYSTEM == "cuda": + moe_kernels = load_kernel(module="moe", repo_id="kernels-community/moe") else: - from moe_kernels.fused_moe import fused_moe + import moe_kernels class UnquantizedSparseMoELayer(nn.Module): @@ -63,7 +66,17 @@ class UnquantizedSparseMoELayer(nn.Module): ) def forward(self, x: torch.Tensor, *, gating_output: torch.Tensor) -> torch.Tensor: - if SYSTEM == "ipex": + if SYSTEM == "rocm": + return moe_kernels.fused_moe( + x, + self.gate_up_proj, + self.down_proj, + gating_output, + self.topk, + renormalize=self.renormalize, + inplace=True, + ) + elif SYSTEM == "ipex": return self.ipex_fused_moe( hidden_states=x, router_logits=gating_output, @@ -146,3 +159,110 @@ def _load_expert_weights_row( assert all_weight is not None return all_weight + + +def fused_moe( + hidden_states: torch.Tensor, + w1: torch.Tensor, + w2: torch.Tensor, + gating_output: torch.Tensor, + topk: int, + renormalize: bool, + inplace: bool = False, + use_grouped_topk: bool = False, + num_expert_group: Optional[int] = None, + topk_group: Optional[int] = None, + custom_routing_function: Optional[Callable] = None, + scoring_func: str = "softmax", + e_score_correction_bias: Optional[torch.Tensor] = None, + use_fp8_w8a8: bool = False, + use_int8_w8a16: bool = False, + use_int4_w4a16: bool = False, + w1_scale: Optional[torch.Tensor] = None, + w2_scale: Optional[torch.Tensor] = None, + a1_scale: Optional[torch.Tensor] = None, + a2_scale: Optional[torch.Tensor] = None, + block_shape: Optional[List[int]] = None, +) -> torch.Tensor: + """ + This function computes a Mixture of Experts (MoE) layer using two sets of + weights, w1 and w2, and top-k gating mechanism. + + Parameters: + - hidden_states (torch.Tensor): The input tensor to the MoE layer. + - w1 (torch.Tensor): The first set of expert weights. + - w2 (torch.Tensor): The second set of expert weights. + - gating_output (torch.Tensor): The output of the gating operation + (before softmax). + - topk (int): The number of top-k experts to select. + - renormalize (bool): If True, renormalize the top-k weights to sum to 1. + - inplace (bool): If True, perform the operation in-place. + Defaults to False. + - num_expert_group: Optional[int]: additional parameter for grouped_topk + - topk_group: Optional[int]: additional parameter for grouped_topk + - use_grouped_topk: If True, use grouped_topk instead of fused_topk + note: Deepseekv2 model uses grouped_topk + - use_fp8_w8a8 (bool): If True, use fp8 arithmetic to compute the inner + products for w1 and w2. Defaults to False. + - use_int8_w8a16 (bool): If True, use fp8 arithmetic to compute the inner + products for w1 and w2. Defaults to False. + - use_int4_w4a16 (bool): If True, use matmul of int4 weight and bf16/fp16 + activation to compute the inner products for w1 and w2. + Defaults to False. + - w1_scale (Optional[torch.Tensor]): Optional scale to be used for + w1. + - w2_scale (Optional[torch.Tensor]): Optional scale to be used for + w2. + - a1_scale (Optional[torch.Tensor]): Optional scale to be used for + a1. + - a2_scale (Optional[torch.Tensor]): Optional scale to be used for + a2. + - block_shape: (Optional[List[int]]): Optional block size for block-wise + quantization. + Returns: + - torch.Tensor: The output tensor after applying the MoE layer. + """ + # Check constraints. + assert gating_output.shape[1] == w1.shape[0], "Number of experts mismatch" + + if use_grouped_topk: + assert num_expert_group is not None and topk_group is not None + from loguru import logger + import inspect + + logger.info(f"{inspect.signature(moe_kernels.grouped_topk)}") + topk_weights, topk_ids = moe_kernels.grouped_topk( + hidden_states, + gating_output, + topk, + renormalize, + num_expert_group, + topk_group, + scoring_func=scoring_func, + e_score_correction_bias=e_score_correction_bias, + ) + elif custom_routing_function is None: + topk_weights, topk_ids = moe_kernels.fused_topk( + hidden_states, gating_output, topk, renormalize + ) + else: + topk_weights, topk_ids = custom_routing_function( + hidden_states, gating_output, topk, renormalize + ) + + return moe_kernels.fused_experts( + hidden_states, + w1, + w2, + topk_weights, + topk_ids, + inplace=inplace, + use_fp8_w8a8=use_fp8_w8a8, + use_int8_w8a16=use_int8_w8a16, + use_int4_w4a16=use_int4_w4a16, + w1_scale=w1_scale, + w2_scale=w2_scale, + a1_scale=a1_scale, + a2_scale=a2_scale, + block_shape=block_shape, + ) diff --git a/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py b/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py index aa032782..225fae77 100644 --- a/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py @@ -22,11 +22,14 @@ from transformers.configuration_utils import PretrainedConfig from typing import Optional, List, Tuple, Any from text_generation_server.layers.attention.kv_cache import get_kv_scales from text_generation_server.utils.import_utils import SYSTEM +from text_generation_server.utils.kernels import load_kernel if SYSTEM == "ipex": from intel_extension_for_pytorch.llm.modules import GatedMLPMOE +elif SYSTEM == "cuda": + moe_kernels = load_kernel(module="moe", repo_id="kernels-community/moe") else: - from moe_kernels.fused_moe import fused_moe + import moe_kernels from text_generation_server.layers.attention import ( paged_attention, @@ -510,7 +513,7 @@ class BlockSparseMoE(nn.Module): topk_group=None, ) else: - out = fused_moe( + out = moe_kernels.fused_moe( x, self.wv1, self.w2, diff --git a/server/text_generation_server/utils/kernels.py b/server/text_generation_server/utils/kernels.py new file mode 100644 index 00000000..42745c71 --- /dev/null +++ b/server/text_generation_server/utils/kernels.py @@ -0,0 +1,22 @@ +import importlib + +from loguru import logger +from hf_kernels import load_kernel as hf_load_kernel + +from text_generation_server.utils.log import log_once + + +def load_kernel(*, module: str, repo_id: str): + """ + Load a kernel. First try to load it as the given module (e.g. for + local development), falling back to a locked Hub kernel. + """ + try: + m = importlib.import_module(module) + log_once(logger.info, f"Using local module for `{module}`") + return m + except ModuleNotFoundError: + return hf_load_kernel(repo_id=repo_id) + + +__all__ = ["load_kernel"] diff --git a/server/uv.lock b/server/uv.lock index 5410a58c..284def0b 100644 --- a/server/uv.lock +++ b/server/uv.lock @@ -168,20 +168,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/fe/ba/e2081de779ca30d473f21f5b30e0e737c438205440784c7dfc81efc2b029/async_timeout-5.0.1-py3-none-any.whl", hash = "sha256:39e3809566ff85354557ec2398b55e096c8364bacac9405a7a1fa429e77fe76c", size = 6233 }, ] -[[package]] -name = "attention-kernels" -version = "0.2.0.post2" -source = { url = "https://github.com/danieldk/attention-kernels/releases/download/v0.2.0.post2/attention_kernels-0.2.0.post2+cu123torch2.5-cp39-abi3-linux_x86_64.whl" } -dependencies = [ - { 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