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Remove unneeded changes

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Signed-off-by: Tyler Michael Smith <tyler@neuralmagic.com>
This commit is contained in:
Tyler Michael Smith 2025-02-04 18:13:50 +00:00
parent c33aeecf24
commit f33ec6d2d2
4 changed files with 10 additions and 50 deletions
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49
csrc/cache_kernels.cu
View File

@ -246,52 +246,6 @@ __global__ void reshape_and_cache_flash_kernel(
}
}
template <typename scalar_t, typename cache_t, Fp8KVCacheDataType kv_dt>
__global__ void reshape_and_cache_flash_full_cuda_kernel(
const int32_t* __restrict__ tensorshape,
const scalar_t* __restrict__ key, // [num_tokens, num_heads, head_size]
const scalar_t* __restrict__ value, // [num_tokens, num_heads, head_size]
cache_t* __restrict__ key_cache, // [num_blocks, block_size, num_heads,
// head_size]
cache_t* __restrict__ value_cache, // [num_blocks, block_size, num_heads,
// head_size]
const int64_t* __restrict__ slot_mapping, // [num_tokens]
const int block_stride, const int key_stride, const int value_stride,
const int num_heads, const int head_size, const int block_size,
const float k_scale, const float v_scale) {
const int64_t token_idx = blockIdx.x;
int32_t unpadded_num_tokens = tensorshape[0];
if (token_idx >= unpadded_num_tokens) {
return;
}
const int64_t slot_idx = slot_mapping[token_idx];
const int64_t block_idx = slot_idx / block_size;
const int64_t block_offset = slot_idx % block_size;
const int n = num_heads * head_size;
for (int i = threadIdx.x; i < n; i += blockDim.x) {
const int64_t src_key_idx = token_idx * key_stride + i;
const int64_t src_value_idx = token_idx * value_stride + i;
const int head_idx = i / head_size;
const int head_offset = i % head_size;
const int64_t tgt_key_value_idx = block_idx * block_stride +
block_offset * num_heads * head_size +
head_idx * head_size + head_offset;
scalar_t tgt_key = key[src_key_idx];
scalar_t tgt_value = value[src_value_idx];
if constexpr (kv_dt == Fp8KVCacheDataType::kAuto) {
key_cache[tgt_key_value_idx] = tgt_key;
value_cache[tgt_key_value_idx] = tgt_value;
} else {
key_cache[tgt_key_value_idx] =
fp8::scaled_convert<cache_t, scalar_t, kv_dt>(tgt_key, k_scale);
value_cache[tgt_key_value_idx] =
fp8::scaled_convert<cache_t, scalar_t, kv_dt>(tgt_value, v_scale);
}
}
}
template <typename scalar_t, typename cache_t, Fp8KVCacheDataType kv_dt>
__global__ void concat_and_cache_mla_kernel(
const scalar_t* __restrict__ kv_c, // [num_tokens, kv_lora_rank]
@ -434,6 +388,9 @@ void reshape_and_cache_flash(
CALL_RESHAPE_AND_CACHE_FLASH);
}
// KV_T is the stored data type of kv-cache.
// CACHE_T is the data type of key and value tensors.
// KV_DTYPE is the real data type of kv-cache.
#define CALL_CONCAT_AND_CACHE_MLA(KV_T, CACHE_T, KV_DTYPE) \
vllm::concat_and_cache_mla_kernel<KV_T, CACHE_T, KV_DTYPE> \
<<<grid, block, 0, stream>>>( \

1
vllm/compilation/backends.py
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@ -633,7 +633,6 @@ class VllmBackend:
if not self.compilation_config.use_cudagraph or \
not self.compilation_config.cudagraph_copy_inputs:
# return self.graph
return self.split_gm
# if we need to copy input buffers for cudagraph

3
vllm/config.py
View File

@ -3245,8 +3245,7 @@ class VllmConfig:
# and avoid any potential issues with the inductor.
self.compilation_config.custom_ops = ["none"]
self.compilation_config.use_cudagraph = True
# self.compilation_config.use_inductor = True
self.compilation_config.use_inductor = False
self.compilation_config.use_inductor = True
self.compilation_config.cudagraph_num_of_warmups = 1
self.compilation_config.pass_config.enable_fusion = False
self.compilation_config.pass_config.enable_reshape = False

7
vllm/v1/worker/gpu_model_runner.py
View File

@ -460,9 +460,14 @@ class GPUModelRunner:
self.slot_mapping_cpu[:total_num_scheduled_tokens],
non_blocking=True)
# TODO: Which of these are needed?
self.seq_lens[num_reqs:].fill_(0)
self.query_start_loc[num_reqs + 1:].fill_(-1)
self.positions[total_num_scheduled_tokens:].fill_(0)
self.slot_mapping[total_num_scheduled_tokens:].fill_(-1)
self.input_batch.block_table.get_device_tensor()[num_reqs:].fill_(-1)
# Fill with -1s -- needed for reshape_and_cache
self.slot_mapping[total_num_scheduled_tokens:].fill_(-1) # Definitely needed
# Prepare for cascade attention if needed.
common_prefix_len = (scheduler_output.num_common_prefix_blocks *