Inc on vLLM - Fix CR comments

setup.py

@@ -238,7 +238,7 @@ def _is_hpu() -> bool:
     is_hpu_available = True
     try:
         subprocess.run(["hl-smi"], capture_output=True, check=True)
-    except (FileNotFoundError, NotADirectoryError, PermissionError, subprocess.CalledProcessError):
+    except (FileNotFoundError, PermissionError, subprocess.CalledProcessError):
         if not os.path.exists('/dev/accel/accel0') and not os.path.exists(
                 '/dev/accel/accel_controlD0'):
             # last resort...
@@ -267,7 +267,7 @@ def _is_neuron() -> bool:
     torch_neuronx_installed = True
     try:
         subprocess.run(["neuron-ls"], capture_output=True, check=True)
-    except (FileNotFoundError, NotADirectoryError, PermissionError, subprocess.CalledProcessError):
+    except (FileNotFoundError, PermissionError, subprocess.CalledProcessError):
         torch_neuronx_installed = False
     return torch_neuronx_installed or VLLM_TARGET_DEVICE == "neuron"
 

vllm/attention/backends/habana_attn.py

@@ -144,11 +144,11 @@ def __init__(
         self.num_heads = num_heads
         self.head_size = head_size
         self.scale = float(scale)
-        self.qk_matmul = Matmul()
+        self.matmul_qk = Matmul()
         self.softmax = Softmax()
-        self.av_matmul = Matmul()
-        self.key_cache = VLLMKVCache()
-        self.value_cache = VLLMKVCache()
+        self.matmul_av = Matmul()
+        self.k_cache = VLLMKVCache()
+        self.v_cache = VLLMKVCache()
         self.num_kv_heads = num_heads if num_kv_heads is None else num_kv_heads
         self.sliding_window = sliding_window
         self.position_bias = None
@@ -212,9 +212,13 @@ def forward(
             # Reshape the input keys and values and store them in the cache.
             # If kv_cache is not provided, the new key and value tensors are
             # not cached. This happens during the initial memory profiling run.
-            num_kv_cache_passes, num_slots_available, indices, offsets = cache_ops.prepare_to_cache(key_cache, attn_metadata.slot_mapping)
-            key_cache = self.key_cache(key, key_cache, num_kv_cache_passes, num_slots_available, indices, offsets)
-            value_cache = self.value_cache(value, value_cache, num_kv_cache_passes, num_slots_available, indices, offsets)
+            num_kv_cache_passes, num_slots_available, indices, offsets = \
+                cache_ops.prepare_to_cache(key_cache,
+                                           attn_metadata.slot_mapping)
+            key_cache = self.k_cache(key, key_cache, num_kv_cache_passes,
+                                     num_slots_available, indices, offsets)
+            value_cache = self.v_cache(value, value_cache, num_kv_cache_passes,
+                                       num_slots_available, indices, offsets)
 
         if attn_metadata.is_prompt:
             # Prompt run.
@@ -240,9 +244,9 @@ def forward(
                     attn_bias=attn_bias,
                     p=0.0,
                     scale=self.scale,
-                    qk_matmul_op=self.qk_matmul,
+                    matmul_qk_op=self.matmul_qk,
                     softmax_op=self.softmax,
-                    av_matmul_op=self.av_matmul,
+                    matmul_av_op=self.matmul_av,
                 )
                 output = out.reshape(batch_size, seq_len, hidden_size)
             else:
@@ -266,8 +270,8 @@ def forward(
                 query, key_cache, value_cache, attn_metadata.block_tables,
                 attn_metadata.seq_lens_tensor, self.kv_cache_dtype,
                 self.num_kv_heads, self.scale, self.position_bias, k_scale,
-                v_scale, self.qk_matmul, self.softmax, self.av_matmul,
-                self.key_cache, self.value_cache)
+                v_scale, self.matmul_qk, self.softmax, self.matmul_av,
+                self.k_cache, self.v_cache)
         # Reshape the output tensor.
         return output.view(batch_size, seq_len, hidden_size)
 

vllm/attention/ops/habana_paged_attn.py

@@ -75,9 +75,9 @@ def forward_decode(
         alibi_slopes: Optional[torch.Tensor],
         k_scale: float,
         v_scale: float,
-        qk_matmul_op,
+        matmul_qk_op,
         softmax_op,
-        av_matmul_op,
+        matmul_av_op,
         k_cache_cls,
         v_cache_cls,
     ) -> torch.Tensor:
@@ -93,9 +93,9 @@ def forward_decode(
             block_size,
             alibi_slopes,
             kv_cache_dtype,
-            qk_matmul_op,
+            matmul_qk_op,
             softmax_op,
-            av_matmul_op,
+            matmul_av_op,
             k_cache_cls,
             v_cache_cls,
         )

vllm/config.py

@@ -474,13 +474,13 @@ def _verify_args(self) -> None:
     def _verify_cache_dtype(self) -> None:
         if self.cache_dtype == "auto":
             pass
-        elif self.cache_dtype in ("fp8", "fp8_e4m3", "fp8_e5m2", "hf8"):
+        elif self.cache_dtype in ("fp8", "fp8_e4m3", "fp8_e5m2", "fp8_inc"):
             logger.info(
                 "Using fp8 data type to store kv cache. It reduces the GPU "
                 "memory footprint and boosts the performance. "
                 "Meanwhile, it may cause accuracy drop without a proper "
                 "scaling factor. "
-                "FP8_E4M3 is also supported on hpu (hf8).")
+                "Intel Gaudi (HPU) supports fp8 (using fp8_inc).")
         else:
             raise ValueError(f"Unknown kv cache dtype: {self.cache_dtype}")
 

vllm/engine/arg_utils.py

@@ -229,12 +229,12 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
         parser.add_argument(
             '--kv-cache-dtype',
             type=str,
-            choices=['auto', 'fp8', 'fp8_e5m2', 'fp8_e4m3', 'hf8'],
+            choices=['auto', 'fp8', 'fp8_e5m2', 'fp8_e4m3', 'fp8_inc'],
             default=EngineArgs.kv_cache_dtype,
             help='Data type for kv cache storage. If "auto", will use model '
             'data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. '
             'ROCm (AMD GPU) supports fp8 (=fp8_e4m3). '
-            'FP8_E4M3 is also supported on hpu (hf8).')
+            'Intel Gaudi (HPU) supports fp8 (using fp8_inc).')
         parser.add_argument(
             '--quantization-param-path',
             type=nullable_str,
@@ -842,7 +842,8 @@ def create_engine_config(self, ) -> EngineConfig:
             self.model_loader_extra_config[
                 "qlora_adapter_name_or_path"] = self.qlora_adapter_name_or_path
 
-        device = device_config.device if self.weights_load_device is None else self.weights_load_device
+        device = device_config.device if self.weights_load_device is None else \
+                 self.weights_load_device
         load_config = LoadConfig(
             load_format=self.load_format,
             download_dir=self.download_dir,

vllm/hpu/cache_ops.py

@@ -64,12 +64,14 @@ def prepare_to_cache(cache, slot_mapping):
     return num_kv_cache_passes, num_slots_available, indices, offsets
 
 
-def insert_or_update_cache(input, cache, num_kv_cache_passes, num_slots_available, block_indices, block_offsets):
+def insert_or_update_cache(input, cache, num_kv_cache_passes,
+                           num_slots_available, block_indices, block_offsets):
     for i in range(num_kv_cache_passes):
         start_idx = i * num_slots_available
         end_idx = (i + 1) * num_slots_available
         cache.index_put_(
-            (block_indices[start_idx:end_idx], block_offsets[start_idx:end_idx]),
+            (block_indices[start_idx:end_idx],
+             block_offsets[start_idx:end_idx]),
             input[start_idx:end_idx])
 
 

vllm/hpu/ops.py

@@ -11,7 +11,6 @@
 import torch
 import torch.nn.functional as F
 
-import vllm.hpu.utils as hpu_utils
 from vllm.logger import init_logger
 
 logger = init_logger()
@@ -43,9 +42,9 @@ def paged_attention_v1(query,
                        block_size,
                        alibi_slopes=None,
                        kv_cache_dtype=None,
-                       qk_matmul_op=torch.matmul,
+                       matmul_qk_op=torch.matmul,
                        softmax_op=torch.softmax,
-                       av_matmul_op=torch.matmul,
+                       matmul_av_op=torch.matmul,
                        k_cache_cls=None,
                        v_cache_cls=None) -> None:
     seq_len = block_tables.size(1)
@@ -60,20 +59,22 @@ def paged_attention_v1(query,
                                  batch_size, 1, 1, -1))
     query.mul_(scale)
     query = query.unsqueeze(-2)
-    fetch_keys = fetch_from_cache if k_cache_cls is None else k_cache_cls.fetch_from_cache
+    fetch_keys = fetch_from_cache if k_cache_cls is None else \
+                 k_cache_cls.fetch_from_cache
     keys = fetch_keys(key_cache, block_tables, (0, 2, 3, 1))
     if query_heads != kv_heads:
         query = query.unflatten(1, (kv_heads, -1))
         keys = [k.unflatten(1, (kv_heads, 1)) for k in keys]
         mask = mask.unsqueeze(2)
 
-    attn_weights = torch.cat([qk_matmul_op(query, k) for k in keys], dim=-1)
+    attn_weights = torch.cat([matmul_qk_op(query, k) for k in keys], dim=-1)
     if alibi_slopes is not None:
         attn_weights.add_(alibi_slopes[:, :, -attn_weights.size(2):,
                                        -attn_weights.size(3):])
     attn_weights = softmax_op(attn_weights.masked_fill(mask, min_inf), dim=-1)
 
-    fetch_values = fetch_from_cache if v_cache_cls is None else k_cache_cls.fetch_from_cache
+    fetch_values = fetch_from_cache if v_cache_cls is None else \
+                   v_cache_cls.fetch_from_cache
     values = fetch_values(value_cache, block_tables, (0, 2, 1, 3))
     if PA_SPLIT_VALUE:
         attn_weights = attn_weights.split(block_size, dim=-1)
@@ -82,7 +83,7 @@ def paged_attention_v1(query,
         attn_weights = [attn_weights]
     if query_heads != kv_heads:
         values = [v.unflatten(1, (kv_heads, 1)) for v in values]
-    attn_weights = [av_matmul_op(a, v) for a, v in zip(attn_weights, values)]
+    attn_weights = [matmul_av_op(a, v) for a, v in zip(attn_weights, values)]
     if query_heads != kv_heads:
         attn_weights = [a.flatten(1, 2) for a in attn_weights]
     attn_weights = sum(attn_weights)
@@ -132,9 +133,9 @@ def prompt_attention(
     attn_bias: Optional[torch.Tensor] = None,
     p: float = 0.0,
     scale: Optional[float] = None,
-    qk_matmul_op = torch.matmul,
+    matmul_qk_op = torch.matmul,
     softmax_op = torch.softmax,
-    av_matmul_op = torch.matmul,
+    matmul_av_op = torch.matmul,
 ) -> torch.Tensor:
     query = query.transpose(1, 2)
     key = key.transpose(1, 2)
@@ -147,11 +148,11 @@ def prompt_attention(
         value = value.unflatten(1, (kv_heads, 1))
         if attn_bias is not None:
             attn_bias = attn_bias.unsqueeze(2)
-    attn_weights = qk_matmul_op(query * scale, key.transpose(-1, -2))
+    attn_weights = matmul_qk_op(query * scale, key.transpose(-1, -2))
     if attn_bias is not None:
         attn_weights.add_(attn_bias)
     attn_weights = softmax_op(attn_weights, dim=-1)
-    attn_weights = av_matmul_op(attn_weights, value)
+    attn_weights = matmul_av_op(attn_weights, value)
     if query_heads != kv_heads:
         attn_weights = attn_weights.flatten(1, 2)
     attn_weights = attn_weights.transpose(1, 2)

vllm/hpu/utils.py

@@ -43,9 +43,13 @@ class VLLMKVCache(torch.nn.Module):
     def __init__(self):
         super(VLLMKVCache, self).__init__()
 
-    def forward(self, input, cache, num_kv_cache_passes, num_slots_available, block_indices, block_offset):
-        insert_or_update_cache(input, cache, num_kv_cache_passes, num_slots_available, block_indices, block_offset)
+    def forward(self, input, cache, num_kv_cache_passes, num_slots_available,
+                block_indices, block_offset):
+        insert_or_update_cache(input, cache, num_kv_cache_passes,
+                               num_slots_available, block_indices,
+                               block_offset)
         return cache
 
     def fetch_from_cache(self, cache, blocks, permutations):
-        return [cache.index_select(0, blocks[:, i]).permute(permutations) for i in range(blocks.size(1))]
+        return [cache.index_select(0, blocks[:, i]).permute(permutations)
+                for i in range(blocks.size(1))]

vllm/model_executor/layers/quantization/inc.py

@@ -1,11 +1,9 @@
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional
 
 import torch
-from torch.nn import Module
 from torch.nn.parameter import Parameter
 import torch.nn.functional as F
 
-from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
 from vllm.model_executor.layers.quantization.base_config import (
@@ -59,7 +57,8 @@ def get_quant_method(self, layer: torch.nn.Module,
     def get_scaled_act_names(self) -> List[str]:
         return []
 
-    def get_min_capability(self) -> int:
+    @classmethod
+    def get_min_capability(cls) -> int:
         # The AWQ kernel only supports Turing or newer GPUs.
         return 75
 
@@ -83,7 +82,8 @@ class INCLinearMethod(LinearMethodBase):
         quant_config: The quantization config.
     """
 
-    def __init__(self, quant_config: INCConfig, separate_bias_add: bool = False):
+    def __init__(self, quant_config: INCConfig,
+                 separate_bias_add: bool = False):
         self.separate_bias_add = separate_bias_add
         self.quant_config = quant_config
 

vllm/model_executor/model_loader/loader.py

@@ -53,8 +53,8 @@ def _get_quantization_config(
             capability = capability[0] * 10 + capability[1]
             if capability < quant_config.get_min_capability():
                 raise ValueError(
-                    f"The quantization method {model_config.quantization} is not "
-                    "supported for the current GPU. "
+                    f"The quantization method {model_config.quantization} "
+                     "is not supported for the current GPU. "
                     f"Minimum capability: {quant_config.get_min_capability()}. "
                     f"Current capability: {capability}.")
         supported_dtypes = quant_config.get_supported_act_dtypes()

vllm/model_executor/models/llama.py

@@ -49,7 +49,8 @@
     default_weight_loader, kv_cache_scales_loader, maybe_remap_kv_scale_name)
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors, SamplerOutput
-from vllm.utils import is_hip, is_hpu
+from vllm.utils import is_hip
+from vllm.platforms import current_platform
 
 from .interfaces import SupportsLoRA
 from .utils import PPMissingLayer, is_pp_missing_parameter, make_layers
@@ -317,7 +318,7 @@ def forward(
             hidden_states = intermediate_tensors["hidden_states"]
             residual = intermediate_tensors["residual"]
 
-        if is_hpu():
+        if current_platform.is_hpu():
             import habana_frameworks.torch as htorch
             htorch.core.mark_step()
         for i in range(self.start_layer, self.end_layer):
@@ -329,7 +330,7 @@ def forward(
                 attn_metadata,
                 residual,
             )
-            if is_hpu():
+            if current_platform.is_hpu():
                 htorch.core.mark_step()
 
         if not get_pp_group().is_last_rank:

vllm/utils.py

@@ -39,7 +39,7 @@
     "fp8": torch.uint8,
     "fp8_e4m3": torch.uint8,
     "fp8_e5m2": torch.uint8,
-    "hf8": torch.float8_e4m3fn,
+    "fp8_inc": torch.float8_e4m3fn,
 }
 
 TORCH_DTYPE_TO_NUMPY_DTYPE = {

vllm/worker/cache_engine.py

@@ -91,7 +91,8 @@ def _allocate_kv_cache(
                 # null block in CpuGpuBlockAllocator requires at least that
                 # block to be zeroed-out.
                 # We zero-out everything for simplicity.
-                dtype = torch.int8 if self.dtype == torch.float8_e4m3fn else self.dtype
+                dtype = torch.uint8 if self.dtype == torch.float8_e4m3fn else \
+                        self.dtype
                 kv_cache.append(
                     torch.zeros(kv_cache_shape,
                                 dtype=dtype,