Refactor layers. (#1866)

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Fixes # (issue)


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server/text_generation_server/models/custom_modeling/mamba_modeling.py

@@ -8,12 +8,12 @@ from typing import Optional, Tuple, Any
 from transformers.configuration_utils import PretrainedConfig
 import torch.nn.functional as F
 
-from text_generation_server.utils.layers import (
+from text_generation_server.layers import (
     SpeculativeHead,
     TensorParallelEmbedding,
-    FastRMSNorm,
     FastLinear,
 )
+from text_generation_server.layers.layernorm import FastRMSNorm
 
 from einops import rearrange
 from causal_conv1d import causal_conv1d_fn, causal_conv1d_update

server/text_generation_server/models/custom_modeling/mpt_modeling.py

@@ -17,7 +17,7 @@ from transformers.modeling_outputs import (
 )
 from einops import rearrange
 from packaging import version
-from text_generation_server.utils.layers import (
+from text_generation_server.layers import (
     TensorParallelEmbedding,
     TensorParallelColumnLinear,
     TensorParallelRowLinear,

server/text_generation_server/models/custom_modeling/neox_modeling.py

@@ -40,7 +40,7 @@ from transformers.modeling_outputs import (
 from transformers.modeling_utils import PreTrainedModel
 from transformers import GPTNeoXConfig
 from loguru import logger
-from text_generation_server.utils.layers import (
+from text_generation_server.layers import (
     TensorParallelColumnLinear,
     TensorParallelEmbedding,
     TensorParallelRowLinear,

server/text_generation_server/models/custom_modeling/opt_modeling.py

@@ -27,7 +27,7 @@ from transformers.modeling_outputs import (
 )
 from transformers.modeling_utils import PreTrainedModel
 from transformers import OPTConfig
-from text_generation_server.utils.layers import (
+from text_generation_server.layers import (
     FastLinear,
     TensorParallelColumnLinear,
     TensorParallelEmbedding,

server/text_generation_server/models/custom_modeling/phi_modeling.py

@@ -9,7 +9,7 @@ from typing import Optional, List, Tuple, Any
 from transformers.configuration_utils import PretrainedConfig
 from transformers.modeling_outputs import CausalLMOutputWithPast
 
-from text_generation_server.utils.layers import (
+from text_generation_server.layers import (
     TensorParallelRowLinear,
     TensorParallelColumnLinear,
     TensorParallelEmbedding,

server/text_generation_server/models/custom_modeling/t5_modeling.py

@@ -38,7 +38,7 @@ from transformers.utils import (
     is_torch_fx_proxy,
 )
 from transformers import T5Config
-from text_generation_server.utils.layers import (
+from text_generation_server.layers import (
     TensorParallelColumnLinear,
     TensorParallelEmbedding,
     TensorParallelRowLinear,

server/text_generation_server/models/flash_causal_lm.py

@@ -12,7 +12,6 @@ from dataclasses import dataclass
 from opentelemetry import trace
 from transformers import PreTrainedTokenizerBase
 from typing import Optional, Tuple, List, Type, Dict
-
 from text_generation_server.models import Model
 from text_generation_server.utils.tokens import batch_top_tokens
 from text_generation_server.utils.speculate import get_speculate
@@ -32,13 +31,14 @@ from text_generation_server.models.globals import MEM_POOL, CUDA_GRAPHS
 from text_generation_server.utils import StoppingCriteria, HeterogeneousNextTokenChooser
 from text_generation_server.utils.dist import MEMORY_FRACTION
 
-tracer = trace.get_tracer(__name__)
 from text_generation_server.utils.import_utils import (
-    IS_CUDA_SYSTEM,
-    IS_ROCM_SYSTEM,
-    IS_XPU_SYSTEM,
+    empty_cache,
+    synchronize,
+    get_free_memory,
 )
 
+tracer = trace.get_tracer(__name__)
+
 
 @dataclass
 class FlashCausalLMBatch(Batch):
@@ -757,10 +757,8 @@ class FlashCausalLM(Model):
 
     def warmup(self, batch: FlashCausalLMBatch):
         # The warmup batch is the biggest batch we could ever receive
-        if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM:
-            torch.cuda.empty_cache()
-        elif IS_XPU_SYSTEM:
-            torch.xpu.empty_cache()
+        empty_cache()
+
         try:
             cache_manager = set_cache_manager(
                 batch.blocks,
@@ -780,10 +778,7 @@ class FlashCausalLM(Model):
                 f"You need to decrease `--max-batch-prefill-tokens`"
             ) from e
 
-        if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM:
-            torch.cuda.synchronize(self.device)
-        elif IS_XPU_SYSTEM:
-            torch.xpu.synchronize(self.device)
+        synchronize(self.device)
 
         # Inspired by the original implementation in [vllm](https://github.com/vllm-project/vllm)
         # Calculate the number of blocks that can be allocated with the free memory
@@ -791,20 +786,7 @@ class FlashCausalLM(Model):
         cache_block_size = BLOCK_SIZE * self.num_kv_heads * self.head_size
         total_cache_size = self.num_layers * cache_block_size * 2 * dtype_size
 
-        if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM:
-            total_free_memory, _ = torch.cuda.mem_get_info(self.device)
-            total_gpu_memory = torch.cuda.get_device_properties(
-                self.device
-            ).total_memory
-
-            free_memory = max(
-                0, total_free_memory - (1 - MEMORY_FRACTION) * total_gpu_memory
-            )
-        elif IS_XPU_SYSTEM:
-            total_gpu_memory = torch.xpu.get_device_properties(self.device).total_memory
-            free_memory = int(total_gpu_memory * 0.5)
-        else:
-            raise NotImplementedError("FlashModel is only available on GPU")
+        free_memory = get_free_memory(self.device, MEMORY_FRACTION)
 
         num_blocks = (
             # Leave 5% for some wiggle room

server/text_generation_server/models/flash_llama.py

@@ -18,7 +18,7 @@ from text_generation_server.utils import (
 
 tracer = trace.get_tracer(__name__)
 
-from text_generation_server.utils.import_utils import IS_XPU_SYSTEM
+from text_generation_server.utils.import_utils import SYSTEM
 
 
 class FlashLlama(FlashCausalLM):
@@ -35,7 +35,7 @@ class FlashLlama(FlashCausalLM):
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
             dtype = torch.float16 if dtype is None else dtype
-        elif IS_XPU_SYSTEM:
+        elif SYSTEM == "xpu":
             device = torch.device(f"xpu:{rank}")
             dtype = torch.float16 if dtype is None else dtype
         else:

server/text_generation_server/models/flash_mistral.py

@@ -33,7 +33,7 @@ tracer = trace.get_tracer(__name__)
 # Will be set in init
 SLIDING_WINDOW: Optional[int] = None
 SLIDING_WINDOW_BLOCKS: Optional[int] = None
-from text_generation_server.utils.import_utils import IS_XPU_SYSTEM
+from text_generation_server.utils.import_utils import SYSTEM
 
 MEM_POOL = torch.cuda.graph_pool_handle() if torch.cuda.is_available() else None
 
@@ -322,7 +322,7 @@ class BaseFlashMistral(FlashCausalLM):
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
             dtype = torch.float16 if dtype is None else dtype
-        elif IS_XPU_SYSTEM:
+        elif SYSTEM == "xpu":
             device = torch.device(f"xpu:{rank}")
             dtype = torch.float16 if dtype is None else dtype
         else:

server/text_generation_server/models/flash_neox.py

@@ -14,7 +14,7 @@ from text_generation_server.utils import (
     weight_files,
     Weights,
 )
-from text_generation_server.utils.import_utils import IS_XPU_SYSTEM
+from text_generation_server.utils.import_utils import SYSTEM
 
 tracer = trace.get_tracer(__name__)
 
@@ -33,7 +33,7 @@ class FlashNeoXSharded(FlashCausalLM):
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
             dtype = torch.float16 if dtype is None else dtype
-        elif IS_XPU_SYSTEM:
+        elif SYSTEM == "xpu":
             device = torch.device(f"xpu:{rank}")
             dtype = torch.float16 if dtype is None else dtype
         else:

server/text_generation_server/models/flash_rw.py

@@ -15,7 +15,7 @@ from text_generation_server.utils import (
     weight_files,
     Weights,
 )
-from text_generation_server.utils.import_utils import IS_XPU_SYSTEM
+from text_generation_server.utils.import_utils import SYSTEM
 
 tracer = trace.get_tracer(__name__)
 
@@ -34,7 +34,7 @@ class FlashRWSharded(FlashCausalLM):
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
             dtype = torch.float16 if dtype is None else dtype
-        elif IS_XPU_SYSTEM:
+        elif SYSTEM == "xpu":
             device = torch.device(f"xpu:{rank}")
             dtype = torch.float16 if dtype is None else dtype
         else:

server/text_generation_server/models/flash_santacoder.py

@@ -18,7 +18,7 @@ from text_generation_server.utils import (
     Weights,
 )
 
-from text_generation_server.utils.import_utils import IS_XPU_SYSTEM
+from text_generation_server.utils.import_utils import SYSTEM
 
 tracer = trace.get_tracer(__name__)
 
@@ -37,7 +37,7 @@ class FlashSantacoderSharded(FlashCausalLM):
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
             dtype = torch.float16 if dtype is None else dtype
-        elif IS_XPU_SYSTEM:
+        elif SYSTEM == "xpu":
             device = torch.device(f"xpu:{rank}")
             dtype = torch.float16 if dtype is None else dtype
         else:

server/text_generation_server/server.py

@@ -85,7 +85,7 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
                 # When using GPTQ, Exllama kernels need some global kernels
                 # For which we have the finale shapes only after the model has loaded
                 # This will allocate those buffers.
-                from text_generation_server.utils.layers import (
+                from text_generation_server.layers.gptq import (
                     create_exllama_buffers,
                     set_device,
                 )

server/text_generation_server/utils/flash_attn.py

@@ -2,13 +2,8 @@ import os
 import torch
 
 from loguru import logger
-import math
 
-from text_generation_server.utils.import_utils import (
-    IS_CUDA_SYSTEM,
-    IS_ROCM_SYSTEM,
-    IS_XPU_SYSTEM,
-)
+from text_generation_server.utils.import_utils import SYSTEM
 
 if os.getenv("USE_FLASH_ATTENTION", "").lower() == "false":
     raise ImportError("`USE_FLASH_ATTENTION` is false.")
@@ -16,10 +11,45 @@ HAS_FLASH_ATTN = True
 HAS_FLASH_ATTN_V2_CUDA = False
 HAS_FLASH_ATTN_V2_ROCM = False
 
-if IS_XPU_SYSTEM:
+if SYSTEM == "xpu":
     import intel_extension_for_pytorch as ipex
 
-if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM:
+    def attention(
+        q,
+        k,
+        v,
+        out,
+        cu_seqlens,
+        max_s,
+        softmax_scale,
+        window_size_left=-1,
+    ):
+        if window_size_left <= 0 and window_size_left != -1:
+            raise ValueError("`window_size_left` must be > 0 or -1")
+
+        if window_size_left != -1:
+            raise ValueError(
+                f"XPU version of Flash Attention does not support window attention (window_size_left != -1, got window_size_left={window_size_left})."
+            )
+        return ipex.llm.functional.varlen_attention(
+            q,
+            k,
+            v,
+            out,
+            cu_seqlens,
+            cu_seqlens,
+            max_s,
+            max_s,
+            0.0,
+            softmax_scale,
+            False,
+            True,
+            False,
+            None,
+        )
+
+
+if SYSTEM in {"cuda", "rocm"}:
     if not torch.cuda.is_available():
         raise ImportError("CUDA is not available")
 
@@ -35,11 +65,7 @@ if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM:
         try:
             import flash_attn_2_cuda
         except ImportError:
-            architecture_suffix = ""
-            if IS_CUDA_SYSTEM:
-                architecture_suffix = "-cuda"
-            elif IS_ROCM_SYSTEM:
-                architecture_suffix = "-rocm"
+            architecture_suffix = f"-{SYSTEM}"
             raise ImportError(
                 "Flash Attention V2 is not installed.\n"
                 "Use the official Docker image (ghcr.io/huggingface/text-generation-inference:latest) "
@@ -50,8 +76,8 @@ if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM:
                 f"GPU with CUDA capability {major} {minor} is not supported for "
                 "Flash Attention V2"
             )
-        HAS_FLASH_ATTN_V2_CUDA = IS_CUDA_SYSTEM
-        HAS_FLASH_ATTN_V2_ROCM = IS_ROCM_SYSTEM
+        HAS_FLASH_ATTN_V2_CUDA = SYSTEM == "cuda"
+        HAS_FLASH_ATTN_V2_ROCM = SYSTEM == "rocm"
     except ImportError as e:
         try:
             import flash_attn_cuda
@@ -62,11 +88,11 @@ if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM:
                 "or install flash attention with `cd server && make install install-flash-attention`"
             ) from e
 
-        if IS_CUDA_SYSTEM and not (is_sm75 or is_sm8x or is_sm90):
+        if SYSTEM == "cuda" and not (is_sm75 or is_sm8x or is_sm90):
             raise ImportError(
                 f"GPU with CUDA capability {major} {minor} is not supported"
             ) from e
-        elif IS_ROCM_SYSTEM:
+        elif SYSTEM == "rocm":
             for idx in range(torch.cuda.device_count()):
                 if "MI210" not in torch.cuda.get_device_name(
                     idx
@@ -79,42 +105,20 @@ if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM:
         HAS_FLASH_ATTN = True
 
 
-def attention(
-    q,
-    k,
-    v,
-    out,
-    cu_seqlens,
-    max_s,
-    softmax_scale,
-    window_size_left=-1,
-):
-    if window_size_left <= 0 and window_size_left != -1:
-        raise ValueError("`window_size_left` must be > 0 or -1")
-
-    if IS_XPU_SYSTEM:
-        if window_size_left != -1:
-            raise ValueError(
-                f"XPU version of Flash Attention does not support window attention (window_size_left != -1, got window_size_left={window_size_left})."
-            )
-        return ipex.llm.functional.varlen_attention(
-            q,
-            k,
-            v,
-            out,
-            cu_seqlens,
-            cu_seqlens,
-            max_s,
-            max_s,
-            0.0,
-            softmax_scale,
-            False,
-            True,
-            False,
-            None,
-        )
-
-    if HAS_FLASH_ATTN_V2_CUDA:
+if HAS_FLASH_ATTN_V2_CUDA:
+
+    def attention(
+        q,
+        k,
+        v,
+        out,
+        cu_seqlens,
+        max_s,
+        softmax_scale,
+        window_size_left=-1,
+    ):
+        if window_size_left <= 0 and window_size_left != -1:
+            raise ValueError("`window_size_left` must be > 0 or -1")
         return flash_attn_2_cuda.varlen_fwd(
             q,
             k,
@@ -136,7 +140,21 @@ def attention(
             False,
             None,
         )
-    elif HAS_FLASH_ATTN_V2_ROCM:
+
+elif HAS_FLASH_ATTN_V2_ROCM:
+
+    def attention(
+        q,
+        k,
+        v,
+        out,
+        cu_seqlens,
+        max_s,
+        softmax_scale,
+        window_size_left=-1,
+    ):
+        if window_size_left <= 0 and window_size_left != -1:
+            raise ValueError("`window_size_left` must be > 0 or -1")
         if window_size_left != -1:
             raise ValueError(
                 f"RoCm version of Flash Attention v2 does not support window attention (window_size_left != -1, got window_size_left={window_size_left})."
@@ -159,7 +177,19 @@ def attention(
             False,
             None,
         )
-    elif HAS_FLASH_ATTN:
+
+elif HAS_FLASH_ATTN:
+
+    def attention(
+        q,
+        k,
+        v,
+        out,
+        cu_seqlens,
+        max_s,
+        softmax_scale,
+        window_size_left=-1,
+    ):
         if window_size_left != -1:
             raise NotImplementedError(
                 "window_size_left is only available with flash attn v2"
@@ -209,4 +239,5 @@ def attention(
             None,
         )
 
+else:
     raise NotImplementedError("flash attention is not installed")

server/text_generation_server/utils/import_utils.py

@@ -10,6 +10,41 @@ def is_xpu_available():
     return hasattr(torch, "xpu") and torch.xpu.is_available()
 
 
-IS_ROCM_SYSTEM = torch.version.hip is not None
-IS_CUDA_SYSTEM = torch.version.cuda is not None
-IS_XPU_SYSTEM = is_xpu_available()
+def get_cuda_free_memory(device, memory_fraction):
+    total_free_memory, _ = torch.cuda.mem_get_info(device)
+    total_gpu_memory = torch.cuda.get_device_properties(device).total_memory
+    free_memory = max(0, total_free_memory - (1 - memory_fraction) * total_gpu_memory)
+    return free_memory
+
+
+def get_xpu_free_memory(device):
+    total_gpu_memory = torch.xpu.get_device_properties(device).total_memory
+    free_memory = int(total_gpu_memory * 0.5)
+    return free_memory
+
+
+SYSTEM = None
+if torch.version.hip is not None:
+    SYSTEM = "rocm"
+    empty_cache = torch.cuda.empty_cache
+    synchronize = torch.cuda.synchronize
+    get_free_memory = get_cuda_free_memory
+elif torch.version.cuda is not None and torch.cuda.is_available():
+    SYSTEM = "cuda"
+    empty_cache = torch.cuda.empty_cache
+    synchronize = torch.cuda.synchronize
+    get_free_memory = get_cuda_free_memory
+elif is_xpu_available():
+    SYSTEM = "xpu"
+    empty_cache = torch.xpu.empty_cache
+    synchronize = torch.xpu.synchronize
+    get_free_memory = get_xpu_free_memory
+else:
+    SYSTEM = "cpu"
+
+    def noop(*args, **kwargs):
+        pass
+
+    empty_cache = noop
+    synchronize = noop
+    get_free_memory = noop

server/text_generation_server/utils/paged_attention.py

 import torch
-from text_generation_server.utils.import_utils import (
-    IS_CUDA_SYSTEM,
-    IS_ROCM_SYSTEM,
-    IS_XPU_SYSTEM,
-)
+from text_generation_server.utils.import_utils import SYSTEM
 
 _PARTITION_SIZE = 512
 
-if IS_XPU_SYSTEM:
+if SYSTEM == "xpu":
     import intel_extension_for_pytorch as ipex
 
 
@@ -18,17 +14,17 @@ def reshape_and_cache(
     value_cache: torch.Tensor,
     slots: torch.Tensor,
 ):
-    if IS_CUDA_SYSTEM:
+    if SYSTEM == "cuda":
         from vllm._C import cache_ops
 
         cache_ops.reshape_and_cache(
             key, value, key_cache, value_cache, slots, "auto", 1.0
         )
-    elif IS_ROCM_SYSTEM:
+    elif SYSTEM == "rocm":
         from vllm import cache_ops
 
         cache_ops.reshape_and_cache(key, value, key_cache, value_cache, slots)
-    elif IS_XPU_SYSTEM:
+    elif SYSTEM == "xpu":
         ipex.llm.modules.PagedAttention.reshape_and_cache(
             key, value, key_cache, value_cache, slots
         )
@@ -68,7 +64,7 @@ def attention(
     block_size = value_cache.shape[3]
     num_seqs, num_heads, head_size = query.shape
     max_num_partitions = (max_s + _PARTITION_SIZE - 1) // _PARTITION_SIZE
-    if IS_XPU_SYSTEM:
+    if SYSTEM == "xpu":
         query = query.contiguous()
         return ipex.llm.modules.PagedAttention.single_query_cached_kv_attention(
             out,
@@ -91,7 +87,7 @@ def attention(
     # to parallelize.
     use_v1 = max_s <= 8192 and (max_num_partitions == 1 or num_seqs * num_heads > 512)
     if use_v1:
-        if IS_CUDA_SYSTEM:
+        if SYSTEM == "cuda":
             from vllm._C import ops
 
             ops.paged_attention_v1(
@@ -109,7 +105,7 @@ def attention(
                 "auto",
                 1.0,
             )
-        elif IS_ROCM_SYSTEM:
+        elif SYSTEM == "rocm":
             from vllm import attention_ops
 
             attention_ops.paged_attention_v1(
@@ -143,7 +139,7 @@ def attention(
         )
         max_logits = torch.empty_like(exp_sums)
 
-        if IS_CUDA_SYSTEM:
+        if SYSTEM == "cuda":
             from vllm._C import ops
 
             ops.paged_attention_v2(
@@ -164,7 +160,7 @@ def attention(
                 "auto",
                 1.0,
             )
-        elif IS_ROCM_SYSTEM:
+        elif SYSTEM == "rocm":
             from vllm import attention_ops
 
             attention_ops.paged_attention_v2(

server/text_generation_server/utils/weights.py

@@ -171,7 +171,7 @@ class Weights:
                 log_once(
                     logger.info, "Converting AWQ model to Exllama/GPTQ packing format."
                 )
-                from text_generation_server.utils.awq.conversion_utils import (
+                from text_generation_server.layers.awq.conversion_utils import (
                     fast_awq_to_gptq,
                 )
 
@@ -227,7 +227,7 @@ class Weights:
 
             bits, groupsize, desc_act, quant_method = self._get_gptq_params()
 
-            from text_generation_server.utils.layers import HAS_EXLLAMA
+            from text_generation_server.layers.gptq import HAS_EXLLAMA
 
             use_exllama = (
                 bits == 4 and HAS_EXLLAMA and quantize == "gptq" and not desc_act
@@ -242,7 +242,7 @@ class Weights:
                 log_once(
                     logger.info, "Converting AWQ model to Exllama/GPTQ packing format."
                 )
-                from text_generation_server.utils.awq.conversion_utils import (
+                from text_generation_server.layers.awq.conversion_utils import (
                     fast_awq_to_gptq,
                 )
 
@@ -321,7 +321,7 @@ class Weights:
                         # it would require to reorder input activations that are split unto several GPUs
                         use_exllama = False
 
-            from text_generation_server.utils.layers import HAS_EXLLAMA, CAN_EXLLAMA
+            from text_generation_server.layers.gptq import HAS_EXLLAMA, CAN_EXLLAMA
 
             if use_exllama:
                 if not HAS_EXLLAMA:
@@ -348,7 +348,7 @@ class Weights:
                 log_once(
                     logger.info, "Converting AWQ model to Exllama/GPTQ packing format."
                 )
-                from text_generation_server.utils.awq.conversion_utils import (
+                from text_generation_server.layers.awq.conversion_utils import (
                     fast_awq_to_gptq,
                 )