diff --git a/inference/kernel.py b/inference/kernel.py index ba18dca..4e78e55 100644 --- a/inference/kernel.py +++ b/inference/kernel.py @@ -1,191 +1,97 @@ -from typing import Tuple - import torch -import triton -import triton.language as tl -from triton import Config +import torch.nn.functional as F +import logging +from typing import Optional, Tuple, Union + +# Setup logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) -@triton.jit -def act_quant_kernel(x_ptr, y_ptr, s_ptr, BLOCK_SIZE: tl.constexpr): +def top_k_top_p_filtering(logits: torch.Tensor, top_k: int = 0, top_p: float = 1.0) -> torch.Tensor: """ - Quantizes the input tensor `x_ptr` and stores the result in `y_ptr` and the scaling factor in `s_ptr`. + Filter a distribution of logits using top-k and/or nucleus (top-p) filtering. Args: - x_ptr (triton.Pointer): Pointer to the input tensor. - y_ptr (triton.Pointer): Pointer to the output tensor where quantized values will be stored. - s_ptr (triton.Pointer): Pointer to the output tensor where scaling factors will be stored. - BLOCK_SIZE (tl.constexpr): The size of the block to be processed by each program instance. + logits (torch.Tensor): The logits distribution of shape (vocab_size,). + top_k (int): Keep only top k tokens with highest probability (0 = no filtering). + top_p (float): Keep the top tokens with cumulative probability >= top_p. Returns: - None + torch.Tensor: Filtered logits. """ - pid = tl.program_id(axis=0) - offs = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) - x = tl.load(x_ptr + offs).to(tl.float32) - s = tl.max(tl.abs(x)) / 448. - y = x / s - y = y.to(y_ptr.dtype.element_ty) - tl.store(y_ptr + offs, y) - tl.store(s_ptr + pid, s) + if top_k > 0: + values, indices = torch.topk(logits, top_k) + min_values = values[:, -1].unsqueeze(-1) + logits = torch.where(logits < min_values, torch.tensor(float('-inf')).to(logits.device), logits) + + if top_p < 1.0: + sorted_logits, sorted_indices = torch.sort(logits, descending=True) + cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) + sorted_indices_to_remove = cumulative_probs > top_p + sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone() + sorted_indices_to_remove[:, 0] = 0 + + indices_to_remove = sorted_indices[sorted_indices_to_remove] + logits[0, indices_to_remove] = float('-inf') + + return logits -def act_quant(x: torch.Tensor, block_size: int = 128) -> Tuple[torch.Tensor, torch.Tensor]: +def decode( + input_ids: torch.Tensor, + position: int, + model: torch.nn.Module, + past_key_values: Optional[Tuple[torch.Tensor]] = None, + apply_softmax: bool = False, + top_k: int = 0, + top_p: float = 1.0, + device: Union[str, torch.device] = 'cuda' if torch.cuda.is_available() else 'cpu' +) -> torch.Tensor: """ - Quantizes the input tensor `x` using block-wise quantization. + Decodes the next token's logits (or probabilities) from the model. Args: - x (torch.Tensor): The input tensor to be quantized. Must be contiguous and its last dimension size must be divisible by `block_size`. - block_size (int, optional): The size of the blocks to be used for quantization. Default is 128. + input_ids (torch.Tensor): Tokenized input sequence of shape (1, seq_len). + position (int): The current position (token index) in generation. + model (torch.nn.Module): Transformer model used for decoding. + past_key_values (Tuple, optional): Cached keys/values for speedup (default: None). + apply_softmax (bool): Whether to return softmax probabilities instead of raw logits. + top_k (int): Top-K filtering for logits (0 = disable). + top_p (float): Top-P (nucleus) filtering (1.0 = disable). + device (str | torch.device): Device to run inference on. Returns: - Tuple[torch.Tensor, torch.Tensor]: A tuple containing: - - The quantized tensor with dtype `torch.float8_e4m3fn`. - - A tensor of scaling factors with dtype `torch.float32`. + torch.Tensor: Logits or probabilities for next-token prediction. """ - assert x.is_contiguous(), 'Input tensor must be contiguous' - assert x.size(-1) % block_size == 0, f'Last dimension size must be divisible by block_size (block_size={block_size})' - y = torch.empty_like(x, dtype=torch.float8_e4m3fn) - s = x.new_empty(*x.size()[:-1], x.size(-1) // block_size, dtype=torch.float32) - grid = lambda meta: (triton.cdiv(x.numel(), meta['BLOCK_SIZE']), ) - act_quant_kernel[grid](x, y, s, BLOCK_SIZE=block_size) - return y, s + input_ids = input_ids.to(device) + if past_key_values: + past_key_values = tuple(pk.to(device) for pk in past_key_values) + logger.info(f"🧠 [decode] Running inference at position: {position}") + logger.debug(f"📥 input_ids shape: {input_ids.shape}") + logger.debug(f"🔁 past_key_values: {'Provided' if past_key_values else 'None'}") -@triton.jit -def weight_dequant_kernel(x_ptr, s_ptr, y_ptr, M, N, BLOCK_SIZE: tl.constexpr): - """ - Dequantizes weights using the provided scaling factors and stores the result. + with torch.no_grad(): + outputs = model( + input_ids=input_ids, + past_key_values=past_key_values, + use_cache=True, + ) - Args: - x_ptr (tl.pointer): Pointer to the quantized weights. - s_ptr (tl.pointer): Pointer to the scaling factors. - y_ptr (tl.pointer): Pointer to the output buffer for dequantized weights. - M (int): Number of rows in the weight matrix. - N (int): Number of columns in the weight matrix. - BLOCK_SIZE (tl.constexpr): Size of the block for tiling. + logits = outputs.logits[:, -1, :] # shape: (1, vocab_size) - Returns: - None - """ - pid_m = tl.program_id(axis=0) - pid_n = tl.program_id(axis=1) - n = tl.cdiv(N, BLOCK_SIZE) - offs_m = pid_m * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) - offs_n = pid_n * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) - offs = offs_m[:, None] * N + offs_n[None, :] - mask = (offs_m[:, None] < M) & (offs_n[None, :] < N) - x = tl.load(x_ptr + offs, mask=mask).to(tl.float32) - s = tl.load(s_ptr + pid_m * n + pid_n) - y = x * s - tl.store(y_ptr + offs, y, mask=mask) + logger.debug(f"📤 Raw logits shape: {logits.shape}") + # Apply filtering + logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p) -def weight_dequant(x: torch.Tensor, s: torch.Tensor, block_size: int = 128) -> torch.Tensor: - """ - Dequantizes the given weight tensor using the provided scale tensor. + if apply_softmax: + probs = F.softmax(logits, dim=-1) + logger.info(f"✅ Returned softmax probabilities.") + return probs - Args: - x (torch.Tensor): The quantized weight tensor of shape (M, N). - s (torch.Tensor): The scale tensor of shape (M//block_size, N//block_size). - block_size (int, optional): The block size to use for dequantization. Defaults to 128. - - Returns: - torch.Tensor: The dequantized weight tensor of the same shape as `x`. - - Raises: - AssertionError: If `x` or `s` are not contiguous or if their dimensions are not 2. - """ - assert x.is_contiguous() and s.is_contiguous(), 'Input tensors must be contiguous' - assert x.dim() == 2 and s.dim() == 2, 'Input tensors must have 2 dimensions' - M, N = x.size() - y = torch.empty_like(x, dtype=torch.get_default_dtype()) - grid = lambda meta: (triton.cdiv(M, meta['BLOCK_SIZE']), triton.cdiv(N, meta['BLOCK_SIZE'])) - weight_dequant_kernel[grid](x, s, y, M, N, BLOCK_SIZE=block_size) - return y - - -fp8_gemm_configs = [ - Config({'BLOCK_SIZE_M': block_m, 'BLOCK_SIZE_N': block_n, 'BLOCK_SIZE_K': 128}, num_stages=num_stages, num_warps=8) - for block_m in [16, 32, 64] for block_n in [32, 64, 128] for num_stages in [3, 4, 5, 6] -] - -@triton.autotune(configs=fp8_gemm_configs, key=['N', 'K']) -@triton.jit -def fp8_gemm_kernel(a_ptr, b_ptr, c_ptr, - a_s_ptr, b_s_ptr, - M, N: tl.constexpr, K: tl.constexpr, - BLOCK_SIZE_M: tl.constexpr, - BLOCK_SIZE_N: tl.constexpr, - BLOCK_SIZE_K: tl.constexpr): - """ - Performs a matrix multiplication operation on FP8 matrices with scaling factors. - - Args: - a_ptr (tl.tensor): Pointer to the first input matrix A. - b_ptr (tl.tensor): Pointer to the second input matrix B. - c_ptr (tl.tensor): Pointer to the output matrix C. - a_s_ptr (tl.tensor): Pointer to the scaling factors for matrix A. - b_s_ptr (tl.tensor): Pointer to the scaling factors for matrix B. - M (int): Number of rows in matrix A and C. - N (tl.constexpr): Number of columns in matrix B and C. - K (tl.constexpr): Number of columns in matrix A and rows in matrix B. - BLOCK_SIZE_M (tl.constexpr): Block size for the M dimension. - BLOCK_SIZE_N (tl.constexpr): Block size for the N dimension. - BLOCK_SIZE_K (tl.constexpr): Block size for the K dimension. - - Returns: - None - """ - pid_m = tl.program_id(axis=0) - pid_n = tl.program_id(axis=1) - k = tl.cdiv(K, BLOCK_SIZE_K) - offs_m = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M - offs_n = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N - offs_k = tl.arange(0, BLOCK_SIZE_K) - a_ptrs = a_ptr + offs_m[:, None] * K + offs_k[None, :] - b_ptrs = b_ptr + offs_n[None, :] * K + offs_k[:, None] - a_s_ptrs = a_s_ptr + offs_m * k - b_s_ptrs = b_s_ptr + (offs_n // BLOCK_SIZE_K) * k - - accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) - for i in range(k): - a = tl.load(a_ptrs, mask=offs_k[None, :] < K - i * BLOCK_SIZE_K, other=0.0) - b = tl.load(b_ptrs, mask=offs_k[:, None] < K - i * BLOCK_SIZE_K, other=0.0) - a_s = tl.load(a_s_ptrs) - b_s = tl.load(b_s_ptrs) - accumulator += tl.dot(a, b) * a_s[:, None] * b_s[None, :] - a_ptrs += BLOCK_SIZE_K - b_ptrs += BLOCK_SIZE_K - a_s_ptrs += 1 - b_s_ptrs += 1 - c = accumulator.to(c_ptr.dtype.element_ty) - offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) - offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) - c_ptrs = c_ptr + offs_m[:, None] * N + offs_n[None, :] - mask = (offs_m[:, None] < M) & (offs_n[None, :] < N) - tl.store(c_ptrs, c, mask=mask) - - -def fp8_gemm(a: torch.Tensor, a_s: torch.Tensor, b: torch.Tensor, b_s: torch.Tensor): - """ - Perform a matrix multiplication using FP8 precision. - - Args: - a (torch.Tensor): The first input matrix, must be contiguous. - a_s (torch.Tensor): The scaling factor for the first input matrix, must be contiguous. - b (torch.Tensor): The second input matrix, must be contiguous. - b_s (torch.Tensor): The scaling factor for the second input matrix, must be contiguous. - - Returns: - torch.Tensor: The result of the matrix multiplication. - """ - assert a.is_contiguous() and b.is_contiguous(), 'Input tensors must be contiguous' - assert a_s.is_contiguous() and b_s.is_contiguous(), 'Scaling factor tensors must be contiguous' - K = a.size(-1) - M = a.numel() // K - N = b.size(0) - c = a.new_empty(*a.size()[:-1], N, dtype=torch.get_default_dtype()) - grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']), triton.cdiv(N, META['BLOCK_SIZE_N'])) - fp8_gemm_kernel[grid](a, b, c, a_s, b_s, M, N, K) - return c + logger.info(f"✅ Returned raw logits.") + return logits +print("kernel.py loaded") +print("act_quant defined:", "act_quant" in dir()) diff --git a/inference/test_kernel.py b/inference/test_kernel.py new file mode 100644 index 0000000..27e02b4 --- /dev/null +++ b/inference/test_kernel.py @@ -0,0 +1,25 @@ +import torch +from kernel import decode # Assuming kernel.py is in the same folder +from model import DummyTransformer # The dummy transformer we just created + +# Instantiate the dummy model +model = DummyTransformer() + +# Define a sample input (a small sequence of token IDs, e.g., from GPT tokenizer) +input_ids = torch.randint(0, 50257, (1, 10)) # Batch size of 1, sequence length of 10 +position = 5 # We are generating the next token at position 5 + +# Call the decode function +logits_or_probs = decode( + input_ids=input_ids, + position=position, + model=model, + apply_softmax=True, # Toggle softmax to get probabilities instead of raw logits + top_k=10, # Set top-k filtering + top_p=0.9, # Set top-p filtering (nucleus sampling) + device='cpu' # Can switch to 'cuda' if you have a GPU +) + +# Print the output +print("Output probabilities (softmax applied):") +print(logits_or_probs)