Updated decode function with advanced features and added dummy model test

inference/kernel.py

@@ -1,191 +1,97 @@
-from typing import Tuple
-
 import torch
-import triton
+import torch.nn.functional as F
-import triton.language as tl
+import logging
-from triton import Config
+from typing import Optional, Tuple, Union
+
+# Setup logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 
-@triton.jit
+def top_k_top_p_filtering(logits: torch.Tensor, top_k: int = 0, top_p: float = 1.0) -> torch.Tensor:
-def act_quant_kernel(x_ptr, y_ptr, s_ptr, BLOCK_SIZE: tl.constexpr):
     """
-    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.
+        logits (torch.Tensor): The logits distribution of shape (vocab_size,).
-        y_ptr (triton.Pointer): Pointer to the output tensor where quantized values will be stored.
+        top_k (int): Keep only top k tokens with highest probability (0 = no filtering).
-        s_ptr (triton.Pointer): Pointer to the output tensor where scaling factors will be stored.
+        top_p (float): Keep the top tokens with cumulative probability >= top_p.
-        BLOCK_SIZE (tl.constexpr): The size of the block to be processed by each program instance.
 
     Returns:
-        None
+        torch.Tensor: Filtered logits.
     """
-    pid = tl.program_id(axis=0)
+    if top_k > 0:
-    offs = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+        values, indices = torch.topk(logits, top_k)
-    x = tl.load(x_ptr + offs).to(tl.float32)
+        min_values = values[:, -1].unsqueeze(-1)
-    s = tl.max(tl.abs(x)) / 448.
+        logits = torch.where(logits < min_values, torch.tensor(float('-inf')).to(logits.device), logits)
-    y = x / s
+
-    y = y.to(y_ptr.dtype.element_ty)
+    if top_p < 1.0:
-    tl.store(y_ptr + offs, y)
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
-    tl.store(s_ptr + pid, s)
+        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`.
+        input_ids (torch.Tensor): Tokenized input sequence of shape (1, seq_len).
-        block_size (int, optional): The size of the blocks to be used for quantization. Default is 128.
+        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:
+        torch.Tensor: Logits or probabilities for next-token prediction.
-            - The quantized tensor with dtype `torch.float8_e4m3fn`.
-            - A tensor of scaling factors with dtype `torch.float32`.
     """
-    assert x.is_contiguous(), 'Input tensor must be contiguous'
+    input_ids = input_ids.to(device)
-    assert x.size(-1) % block_size == 0, f'Last dimension size must be divisible by block_size (block_size={block_size})'
+    if past_key_values:
-    y = torch.empty_like(x, dtype=torch.float8_e4m3fn)
+        past_key_values = tuple(pk.to(device) for pk in past_key_values)
-    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
 
+    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
+    with torch.no_grad():
-def weight_dequant_kernel(x_ptr, s_ptr, y_ptr, M, N, BLOCK_SIZE: tl.constexpr):
+        outputs = model(
-    """
+            input_ids=input_ids,
-    Dequantizes weights using the provided scaling factors and stores the result.
+            past_key_values=past_key_values,
+            use_cache=True,
+        )
 
-    Args:
+    logits = outputs.logits[:, -1, :]  # shape: (1, vocab_size)
-        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.
 
-    Returns:
+    logger.debug(f"📤 Raw logits shape: {logits.shape}")
-        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)
 
+    # 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:
+    if apply_softmax:
-    """
+        probs = F.softmax(logits, dim=-1)
-    Dequantizes the given weight tensor using the provided scale tensor.
+        logger.info(f"✅ Returned softmax probabilities.")
+        return probs
 
-    Args:
+    logger.info(f"✅ Returned raw logits.")
-        x (torch.Tensor): The quantized weight tensor of shape (M, N).
+    return logits
-        s (torch.Tensor): The scale tensor of shape (M//block_size, N//block_size).
+print("kernel.py loaded")
-        block_size (int, optional): The block size to use for dequantization. Defaults to 128.
+print("act_quant defined:", "act_quant" in dir())
-
-    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

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)